A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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Updated
May 15, 2024 - Python
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
(Deprecated) Scikit-learn integration package for Apache Spark
LAMA - automatic model creation framework
Automated modeling and machine learning framework FEDOT
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Workflow engine for exploration of simulation models using high throughput computing
Framework of intelligent optimization methods iOpt
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Hyperparameter optimization in Julia.
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and s…
This is a solution to a Kaggle competition on predicting claim severity for Allstate Insurance using the Extreme Gradient Boosting (XgBoost) algorithm in R
⚡ Fast Concurrent / Parallel Sorting in Go
Globally Safe Model-free Exploration of Dynamical Systems
Tuning of parameters of ML algorithms to optimise precision/f-score for fault detection in softwares
Machine Learning Project using Kaggle dataset
Robustness of DWT vs DCT is graded based on the quality of extracted watermark. The measure used is the Correlation coefficient (0-100%).
The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.
We compared the predictive accuracy and sparsity of support vector machines and relevance vector machines for a range of synthetic data sets differing in signal-to-noise ratio and other measures of difficulty.
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