Critical Temperature

It is the temperature below which the resistance of a superconductor drops to zero. The given dataset has around 80 features and the task is to predict Tc using the XGBoost algorithm.

Data

The data contains 21,263 unique superconductors. The given Tc values are highly right-skewed. Hence, BoxCox transformation was first applied to distribute the Tc values normally.

A Mutual information-based feature selection method was used to select the top 8 contributing features.

An XGBoost algorithm with the following set of parameters was trained on the training dataset.

parameters = {
    "objective": "reg:squarederror",
    "tree_method": "exact",
    "grow_policy": "lossguide",
    "max_depth": 6,
    "reg_alpha": 4,
    "learning_rate": 0.1,   
    "eval_metric": "rmse",
    "seed": 42
}

Error Scores

The MAE score is 0.967 and the R2 score is 0.878. The skewness of Tc seems to affect the prediction efficiency even after applying the transformation.