6. Decision Tree.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## Decision Trees\n",
    "### Agenda\n",
    "1. Introduction to Decision Trees\n",
    "2. The Decision Tree Algorithms\n",
    "3. Decision Tree for Classification\n",
    "4. Decision Tree for Regression\n",
    "5. Advantages & Limitations of Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Introduction to Decision Trees\n",
    "```\n",
    "* Non-parametric supervised learning method for regression & classification\n",
    "* It's similar to playing 'dumb charades'.\n",
    "* A good algorithm will have less & right questions compared to not-so-good one\n",
    "* The nodes are questions & leafs are prediction\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "play_data = pd.read_csv('tennis.csv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>overcast</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>overcast</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     outlook  temp humidity  windy play\n",
       "0      sunny   hot     high  False   no\n",
       "1      sunny   hot     high   True   no\n",
       "2   overcast   hot     high  False  yes\n",
       "3      rainy  mild     high  False  yes\n",
       "4      rainy  cool   normal  False  yes\n",
       "5      rainy  cool   normal   True   no\n",
       "6   overcast  cool   normal   True  yes\n",
       "7      sunny  mild     high  False   no\n",
       "8      sunny  cool   normal  False  yes\n",
       "9      rainy  mild   normal  False  yes\n",
       "10     sunny  mild   normal   True  yes\n",
       "11  overcast  mild     high   True  yes\n",
       "12  overcast   hot   normal  False  yes\n",
       "13     rainy  mild     high   True   no"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "play_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A decision tree for above data\n",
    "\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/dt4.PNG?raw=true\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Decision Tree Algorithm\n",
    "\n",
    "* Decision Tree is based on (CART) which is advancement of ID3, developed in 1986 by Ross Quinlan.\n",
    "* ID3 works when feature data & target data both are categorical in nature\n",
    "* C4.5 is an advancement of ID3, it coverts continues features into categorical features. Then, proceeds with ID3\n",
    "* CART is based on C4.5, with slight advancement of 'target can be continues'.\n",
    "* scikit-learn decision trees are based on CART"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criterion of creating Decision Tree\n",
    "* Entropy - Objective of CART is to maximize information gain in each split\n",
    "* Gini Impurity - If classes are mixed, gini impurity is maximul\n",
    "##### Both the approaches, yields almost same results. We will discuss algorithm using Entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Entropy of play\n",
    "* Entropy(play) = – p(Yes) . log2p(Yes) – p(No) . log2p(No)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    9\n",
       "no     5\n",
       "Name: play, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data.play.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Entropy_Play = -(9/14)*np.log2(9/14) -(5/14)*np.log2(5/14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "data": {
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       "0.9402859586706311"
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     "execution_count": 8,
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     "output_type": "execute_result"
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   "source": [
    "Entropy_Play"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain\n",
    "* The information gain is based on the decrease in entropy after a dataset is split on an attribute. \n",
    "* Constructing a decision tree is all about finding attribute that returns the highest information gain (i.e., the most homogeneous branches).\n",
    "* Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]\n",
    "* We intend to choose the attribute, splitting by which information gain will be the most\n",
    "* Next step is calculating information gain for all attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Information Gain on splitting by Outlook\n",
    "* Gain(Play, Outlook) = Entropy(Play) – ∑ [ p(Play|Outlook) . Entropy(Play|Outlook) ]\n",
    "* Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] – [ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ]  – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
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       "   outlook  temp humidity  windy play\n",
       "0    sunny   hot     high  False   no\n",
       "1    sunny   hot     high   True   no\n",
       "7    sunny  mild     high  False   no\n",
       "8    sunny  cool   normal  False  yes\n",
       "10   sunny  mild   normal   True  yes"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[play_data.outlook == 'sunny']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entropy(Play|Outlook=Sunny)\n",
    "Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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       "0.9709505944546686"
      ]
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     "execution_count": 20,
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     "output_type": "execute_result"
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   "source": [
    "Entropy_Play_Outlook_Sunny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>overcast</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>overcast</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     outlook  temp humidity  windy play\n",
       "2   overcast   hot     high  False  yes\n",
       "6   overcast  cool   normal   True  yes\n",
       "11  overcast  mild     high   True  yes\n",
       "12  overcast   hot   normal  False  yes"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[play_data.outlook == 'overcast']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entropy(Play|Outlook=overcast)\n",
    "# Since, it's a homogenous data entropy will be 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   outlook  temp humidity  windy play\n",
       "3    rainy  mild     high  False  yes\n",
       "4    rainy  cool   normal  False  yes\n",
       "5    rainy  cool   normal   True   no\n",
       "9    rainy  mild   normal  False  yes\n",
       "13   rainy  mild     high   True   no"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[play_data.outlook == 'rainy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entropy(Play|Outlook=rainy)\n",
    "Entropy_Play_Outlook_Rain = -(2/5)*np.log2(2/5) - (3/5)*np.log2(3/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709505944546686"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Rain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gain on splitting by attribute outlook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24674981977443933"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gain(Play, Outlook) = Entropy(Play) – [ p(Play|Outlook=Sunny) . Entropy(Play|Outlook=Sunny) ] – \n",
    "#[ p(Play|Outlook=Overcast) . Entropy(Play|Outlook=Overcast) ] – [ p(Play|Outlook=Rain) . Entropy(Play|Outlook=Rain) ]\n",
    "\n",
    "Entropy_Play - (5/14)*Entropy_Play_Outlook_Sunny - (4/14)*0 - (5/14) * Entropy_Play_Outlook_Rain "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other gains\n",
    "* Gain(Play, Temperature) - 0.029\n",
    "* Gain(Play, Humidity) - 0.151\n",
    "* Gain(Play, Wind) - 0.048\n",
    "\n",
    "#### Conclusion - Outlook is winner & thus becomes root of the tree\n",
    "<img src=\"https://i1.wp.com/sefiks.com/wp-content/uploads/2017/11/tree-v1.png?zoom=1.25&resize=728%2C252&ssl=1\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time to find the next splitting criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>overcast</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>overcast</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     outlook  temp humidity  windy play\n",
       "2   overcast   hot     high  False  yes\n",
       "6   overcast  cool   normal   True  yes\n",
       "11  overcast  mild     high   True  yes\n",
       "12  overcast   hot   normal  False  yes"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[play_data.outlook == 'overcast']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Conclusion - If outlook is overcast, play is true\n",
    "\n",
    "### Let's find the next splitting feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   outlook  temp humidity  windy play\n",
       "0    sunny   hot     high  False   no\n",
       "1    sunny   hot     high   True   no\n",
       "7    sunny  mild     high  False   no\n",
       "8    sunny  cool   normal  False  yes\n",
       "10   sunny  mild   normal   True  yes"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[play_data.outlook == 'sunny']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entropy(Play_Sunny|)\n",
    "Entropy_Play_Outlook_Sunny =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709505944546686"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Sunny"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain for humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709505944546686"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Entropy for attribute high = 0, also entropy for attribute normal = 0 \n",
    "Entropy_Play_Outlook_Sunny - (3/5)*0 - (2/5)*0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain for windy\n",
    "* False -> 3 -> [1+ 2-]\n",
    "* True -> 2 -> [1+ 1-]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Entropy_Wind_False = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9182958340544896"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Wind_False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01997309402197489"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Sunny - (3/5)* Entropy_Wind_False  - (2/5)*1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain for temperature\n",
    "* hot -> 2 -> [2- 0+]\n",
    "* mild -> 2 -> [1+ 1-]\n",
    "* cool -> 1  -> [1+ 0-]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5709505944546686"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Sunny - (2/5)*0 - (1/5)*0 - (2/5)* 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion : Humidity is the best choice on sunny branch\n",
    "\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/dt3.PNG?raw=true\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  outlook  temp humidity  windy play\n",
       "0   sunny   hot     high  False   no\n",
       "1   sunny   hot     high   True   no\n",
       "7   sunny  mild     high  False   no"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'high')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   outlook  temp humidity  windy play\n",
       "8    sunny  cool   normal  False  yes\n",
       "10   sunny  mild   normal   True  yes"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[(play_data.outlook == 'sunny') & (play_data.humidity == 'normal')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the rainy branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   outlook  temp humidity  windy play\n",
       "3    rainy  mild     high  False  yes\n",
       "4    rainy  cool   normal  False  yes\n",
       "5    rainy  cool   normal   True   no\n",
       "9    rainy  mild   normal  False  yes\n",
       "13   rainy  mild     high   True   no"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_data[play_data.outlook == 'rainy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entropy(Play_Rainy|)\n",
    "Entropy_Play_Outlook_Rainy =-(3/5)*np.log2(3/5) -(2/5)*np.log2(2/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709505944546686"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Rainy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain for temp\n",
    "* mild -> 3 [2+ 1-]\n",
    "* cool -> 2 [1+ 1-]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020150594454668602"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Rainy - (3/5)*0.918 - (2/5)*1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain for Windy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709505944546686"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Rainy - (2/5)*0 - (3/5)*0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information Gain for Humidity\n",
    "* High -> 2 -> [1+ 1-]\n",
    "* Normal -> 3 -> [2+ 1-]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Entropy_Play_Outlook_Rainy_Normal = -(1/3)*np.log2(1/3) - (2/3)*np.log2(2/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9182958340544896"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Rainy_Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01997309402197489"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entropy_Play_Outlook_Rainy - (2/5)*1 - (3/5)*Entropy_Play_Outlook_Rainy_Normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Tree\n",
    "\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/dt4.PNG?raw=true\" width=\"600px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Decision Tree for Classification\n",
    "* The leaf nodes of decision tree decides the class\n",
    "* CART will convert features with continues values into categorical values\n",
    "* Different tree will be generated with same data given in different order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz, ExtraTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(criterion='entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX,trainY,testY = train_test_split(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(dt,'dt.tree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the tree\n",
    "* http://www.webgraphviz.com/\n",
    "\n",
    "* Criteria - Entropy\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/dt6.PNG?raw=true\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 1, 0, 2, 1, 1, 2, 0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 1, 1, 2, 2])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.predict(testX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importances\n",
    "* Important features will be higher up the tree\n",
    "* We can use this techniques to identify important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.0354139 , 0.33263845, 0.63194765])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Decision Decision Boundry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = make_blobs(n_features=2, n_samples=1000, cluster_std=.8, centers=4, random_state=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20f80151278>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f80068da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=Y,s=5, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_step = 0.2\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome = dt.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86, 109)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f8004b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=Y,s=5,cmap='viridis')\n",
    "plt.scatter(xx.ravel(),yy.ravel(),c=outcome,s=1,alpha=1, cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Decision Tree for Regression\n",
    "* Continues target is predicted with Tree.\n",
    "* ID3 is modified for regression by replacing Information Gain with Mean Squared Error.\n",
    "* Decision Tree tries to partition data into subsets of homogenous contents ( minimize mean squared error )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "play_time = pd.read_csv('tennis-time.csv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sunny</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rainy</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>overcast</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>False</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sunny</td>\n",
       "      <td>cool</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sunny</td>\n",
       "      <td>mild</td>\n",
       "      <td>normal</td>\n",
       "      <td>True</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>overcast</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>overcast</td>\n",
       "      <td>hot</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>rainy</td>\n",
       "      <td>mild</td>\n",
       "      <td>high</td>\n",
       "      <td>True</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     outlook  temp humidity  windy  time\n",
       "0      sunny   hot     high  False    26\n",
       "1      sunny   hot     high   True    30\n",
       "2   overcast   hot     high  False    48\n",
       "3      rainy  mild     high  False    46\n",
       "4      rainy  cool   normal  False    62\n",
       "5      rainy  cool   normal   True    23\n",
       "6   overcast  cool   normal   True    43\n",
       "7      sunny  mild     high  False    36\n",
       "8      sunny  cool   normal  False    38\n",
       "9      rainy  mild   normal  False    48\n",
       "10     sunny  mild   normal   True    48\n",
       "11  overcast  mild     high   True    62\n",
       "12  overcast   hot   normal  False    44\n",
       "13     rainy  mild     high   True    30"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for col in ['outlook','temp','humidity','windy']:\n",
    "    le = LabelEncoder()\n",
    "    play_time[col] = le.fit_transform(play_time[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.fit(play_time.drop('time',axis=1), play_time.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(dt,'regtree.dot',feature_names=['outlook','temp','humidity','windy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    outlook  temp  humidity  windy  time\n",
       "0         2     1         0      0    26\n",
       "1         2     1         0      1    30\n",
       "2         0     1         0      0    48\n",
       "3         1     2         0      0    46\n",
       "4         1     0         1      0    62\n",
       "5         1     0         1      1    23\n",
       "6         0     0         1      1    43\n",
       "7         2     2         0      0    36\n",
       "8         2     0         1      0    38\n",
       "9         1     2         1      0    48\n",
       "10        2     2         1      1    48\n",
       "11        0     2         0      1    62\n",
       "12        0     1         1      0    44\n",
       "13        1     2         0      1    30"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/dt8.PNG?raw=true\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.predict([[2,1,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29746223, 0.38824339, 0.14515689, 0.16913749])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Advantages of Decision Tree\n",
    "* Highly Interpretable & can be visualized\n",
    "* Minimal data preprocessing - missing data handling, normalizing, one-hot-encoding not required\n",
    "* Handle both neumerical & categorical values\n",
    "* Supports multi-output "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding Multi-Output Problem\n",
    "* Supervised learning problem with multiple outputs, target is 2D\n",
    "* If there is no correlation between target, they can be independently calculated using N models ( in case of N targets )\n",
    "* Since predictors are same, outputs are likely to be correlated.\n",
    "* Like many other models, decision trees allows for multi-output targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Limitations of Decision Tree\n",
    "* Overfitting - height of tree kept growing with addition of more data\n",
    "* Slight changes in data or order of data can change the tree\n",
    "* Imbalanced classes datasets creates biased tree so data needs balancing\n",
    "\n",
    "##### The above two limitations are handled by ensemble methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pruning\n",
    "* A technique of machine learning which reduces height of the tree by chopping off parts of the tree that's not doing anything significant in prediction\n",
    "* Two types of pruning - Prepruning & Postpruning\n",
    "  - Prepruning : Don't allow tree to grow beyond this point\n",
    "  - Postpruning : Allows tree to grow as much as possible, then prune the tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pruning is not currently supported in scikit\n",
    "* This can be achieved using min_samples_leaf & max_height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/questions-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Conceptual Questions \n",
    "\n",
    "1. Calculate the self entropy of the following coin with bias :  \n",
    "  a. p=0.5  \n",
    "  b. p=0.9  \n",
    "  c. p=1  \n",
    "  \n",
    "2. Calculate the cross entropy of the dice with bias=\\[ 0.1, 0.2, 0.1, 0.15, 0.15, 0.3 \\] in the following cases  :  \n",
    "  a. output=1  \n",
    "  b. output=2  \n",
    "  c. output=6  \n",
    "  \n",
    "3. Tell the cases where you think that cross entropy would be higher for a coin.  \n",
    "  a. p(H)=0.9, outcome=Head  \n",
    "  b. p(H)=0.1, outcome=Tail  \n",
    "  c. p(H)=0.99, outcome=Tail  \n",
    "  \n",
    "4. What is Information Gain in terms of Entropy? In case of predicting the number of wickets, what would be information gain on knowledge of weather? \n",
    "  \n",
    "5. What is Gini's Impurity? Why do we use it more often in popular Decision Tree algorithms like CART, rather then entropy? What does the formula signify?\n",
    "  \n",
    "6. If the size of feature vector for data point is 5, what will be the depth of the tree?  \n",
    "  \n",
    "7. What is the disadvantage of having a tree branched over all the features ? How do we control/prun the growth of tree?  \n",
    "\n",
    "8. Why is there an aversion in the ML community for using decision tree as a primary choice for building a classifier? \n",
    "\n",
    "9. Think about the advantages/Disadvantages of Decision Tree vs Linear Regression.\n",
    "\n",
    "# 2. Programming Questions \n",
    "1. Build a decision Tree Classifier from scratch in Python, Choose the features based on Information Gain\n",
    "2. Plot self entropy and Gini's Impurity against the bias in case of coin tossing experiment\n",
    "3. Try to use Decision Tree Classifier to find out if the person will get high, medium or low salary based on the rest of the features. The data is given bellow. Visualize the same using graphViz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_data = pd.read_csv('https://raw.githubusercontent.com/zekelabs/data-science-complete-tutorial/master/Data/adult.data.txt', names=cols)"
   ]
  }
 ],
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