- class lightautoml.pipelines.features.base.FeaturesPipeline(**kwargs)
Analyze train dataset and create composite transformer based on subset of features. Instance can be interpreted like Transformer (look for
LAMLTransformer) with delayed initialization (based on dataset metadata) Main method, user should define in custom pipeline is
.create_pipeline. For example, look at
LGBSimpleFeatures. After FeaturePipeline instance is created, it is used like transformer with
- property used_features
List of feature names from original dataset that was used to produce output.
Analyse dataset and create composite transformer.
Create pipeline and then fit on train data and then transform.