Welcome to Learn’s documentation!

Learn.jl is a Julia package for machine learning. It provides a common API for various types of machine learning algorithms. Learn.jl is inspired by sklearn and emulates its approach to unify various machine learning algorithms. The overarching goal of Learn.jl is to be the best API for machine learning in Julia by integrating Julia’s machine learning packages in a common framework.

Contents:

• Regression
  • Functions
  • SVR
  • Linear regression
  • Decision tree regression
  • Random forest regression
• Classification
  • Functions
  • Multiclass Strategies
  • Logistic regression
  • Random forest classification
  • Decision tree classification
  • Gaussian naive Bayes
  • Linear discriminant analysis
• Clustering
  • Functions
  • K-means
• Scoring
  • Regression
  • Classification
• Preprocessing
  • Function
  • MinMaxScaler
  • StandardScaler
  • PCA
  • FastICA
• Pipeline
  • Creation
  • Functions
  • Example
• Cross-validation
  • K-fold
  • Stratified K-fold
  • Shuffle-Split
  • cross_val_score!
  • GridSearchCV

Indices and tables

• Index
• Module Index
• Search Page