Xgboost

from xgboost import XGBRegressor, plot_importance

model = XGBRegressor(

    max_depth=17,

    n_estimators=2110,

    colsample_bytree=0.5,

    min_child_weight=330,

    subsample=0.8,    

    eta=0.2,

    objective='reg:squarederror',

    tree_method='gpu_hist')

model.fit(

    X_train, 

    y_train, 

    eval_metric="rmse", 

    eval_set=[(X_train, Y_train), (X_valid, Y_valid)], 

    verbose=True, 

    early_stopping_rounds=10)

plot_features(model)

print(f"Test R2 score: {model.score(X_test, y_test):.2f}")

GPU acceleration

https://github.com/dmlc/xgboost/blob/master/demo/gpu_acceleration/tree_shap.py 

params = {

    "device": "cuda",

}