Automated Machine Learning
Feature Selection

Use only relevant features in your models.
Drop noisy features from the analysis

Features Selection Overview

The feature selection is a process of selecting only relevant features (with signal) for the ML model construction.
The AutoML feature selection works procedure in two steps. First step is to train ML model with random feature inserted to the data. The permutation-based features importance is computed for a such model.
The features with importance smaller than random feature are removed. The ML models are trained only on selected features.

Features Selection in the MLJAR AutoML

Advantages of Features Selection

Automatic Features Selection


Number of selected features are determined automatically.

Categorical data type


Convert categorical features into numeric with label encoder, one-hot encoder or target encoder. Proper encoder type is automatically selected based on feature cardinality and AutoML training stage.

Datetime data type

Date & Time

Convert date and time features into numbers understood by ML algorithms. Get features link year, month, day, weekday, day of the year, hour, and difference to the earliest date automatically extracted from your data.

Check more features engineering methods

Golden Features Search

K-Means Features

Features Preprocessing