Automated Machine Learning
K-Means Features
Use K-Means clustering algorithm to enhance your data
and improve model's performance
K-Means Features Procedure Overview
AutoML computes the K-Means centers based on numeric features.
If needed the scaling is applied.
The information about distance to K-Means centers and center number is added to each sample.
The number of clusters is determined based on number of rows & columns in the training data.
We use Mini-Batch K-Means versions available in the scikit-learn package.
For details please check the source code in the GitHub.
