Automated Machine Learning
Golden Features

Golden Features have great predictive power.
They are created from existing features
by applying mathematical operators

Golden Featues Generation Overview

The mljar AutoML framework uses numeric features in Golden Features search.
From each pair of original features it creates a new feature by using of mathematical operators: +, -, /, *.
The Decision Tree algorithm is used to assess the predictive power of newly created features.
Only top new features are included in the training data.

1. New Features Generation

Golden Features Generation Step

2. Golden Features Selection

Golden Features Selection Step

Golden Features Advantages

Text data type

Discover New Features

Golden Features procedure helps you discover unknown relationships in your data. Let yourself be surprised.

Categorical data type

Improve Peformance

Improve your Machine Learning pipeline accuracy by including Golden Features in your dataset.

Check more features engineering methods

Features Preprocessing

K-Means Features

Features Selection