Welcome to GridGain ML Python API documentation!

Contents:

• Basic Information
  • What is it
  • Prerequisites
  • Installation
    • for end user
    • for developer
• API Specification
  • Core
  • Regression
  • Classification
  • Clustering
  • Preprocessing
  • Model Selection
  • Inference
• Examples of usage
  • Cache API
  • Regression
    • Linear Regression
    • Decision Tree Regression
    • KNN Regression
    • Random Forest Regression
    • MLP Regression
  • Classification
    • Decision Tree Classification
    • ANN Classification
    • KNN Classification
    • LogReg Classification
    • SVM Classification
    • Random Forest Classification
    • MLP Classification
  • Clustering
    • KMeans Clustering
    • GMM Clustering
  • Preprocessing
    • Normalization Preprocessing
    • Binarization Preprocessing
    • Imputing Preprocessing
    • One-Hot-Encoding Preprocessing
    • MinMax Scaling Preprocessing
    • MaxAbs Scaling Preprocessing
  • Model Selection
    • Test/Train Splitting
    • Cross Validation
  • Inference
    • Distributed Inference
    • Model storage

Indices and tables

• Index
• Module Index
• Search Page