getml.predictors

The predictor classes defined in this module serve two purposes.

First, a predictor can be used as a feature selector in Pipeline to only select the best features generated during the automated feature learning and to get rid of any redundancies.

Second, by using it as a predictor, it will be trained on the features of the supplied data set and used to predict unknown results.

Every time a new data set is passed to 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 learning algorithm, and, finally, its target will be predicted using the trained predictor.

The algorithms can be grouped according to their finesse and whether you want to use them for a classification or regression problem. For memory intensive applications, the getML Enterprise edition offers predictors with memory mapping .

	simple	sophisticated	memory intensive
regression	LinearRegression	XGBoostRegressor	ScaleGBMRegressor
classification	LogisticRegression	XGBoostClassifier	ScaleGBMClassifier

Note

All predictors need to be passed to Pipeline.