getml.predictors

This module provides ML algorithms to learn and predict on the generated features.

The predictor classes defined in this module serve two purposes. First, a predictor can be provided as feature_selector in MultirelModel or RelboostModel to only select the best features generated during the automated feature engineering and to get rid off any redundancies. Second, by providing it as a predictor, it will be trained on the features of the supplied data set and used to predict to unknown results. Every time a new data set is provided in the predict() method of one of the models, the raw relational data is interpreted in the data model, which was provided during the construction of the model, transformed into features using the trained feature engineering algorithm, and, finally, its target will be predicted using the trained predictor.

The provided algorithms can be grouped according to their finesse and whether you want to use them for a classification or regression problem.

	simple	sophisticated
regression	LinearRegression	XGBoostRegressor
classification	LogisticRegression	XGBoostClassifier

Note

All predictors will be trained and called entirely within MultirelModel and RelboostModel using their fit(), score(), and predict() methods.

Classes

LinearRegression([learning_rate, reg_lambda])	Simple predictor for regression problems.
LogisticRegression([learning_rate, reg_lambda])	Simple predictor for classification problems.
XGBoostClassifier([booster, …])	Gradient boosting classifier based on xgboost.
XGBoostRegressor([booster, …])	Gradient boosting regressor based on xgboost.