Models

Here are some examples to get you started.

src.models.train_model.model_ensembler(X_train_tfv, X_train_ft, y_train)

Train ensemble model

src.models.train_model.model_lightgbm(X_train, y_train)

Light Gradient Boosting Machine

https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst

src.models.train_model.model_ridge(X_train, y_train)

Ridge regression Minimizing the residual sum of squares we also have a penalty on the coefficients

http://scikit-learn.org/stable/modules/linear_model.html#ridge-regression

src.models.train_model.model_xgb(X_train, y_train)

Extreme gradient boosting

http://xgboost.readthedocs.io/en/latest/

src.models.train_model.score_function(y_true, y_pred)

Score function auc

src.models.train_model.split_train(X, y, test_size, random_state=7)

Train and validation split

Predict functions

src.models.predict_model.predict_test(submission, preds1)

Create submission file from predictions