Categories

• Classification Learner App
  Interactively train, validate, and tune classification models
  
• Classification Trees
  Binary decision trees for multiclass learning
  
• Discriminant Analysis
  Regularized linear and quadratic discriminant analysis
  
• Naive Bayes
  Naive Bayes model with Gaussian, multinomial, or kernel predictors
  
• Nearest Neighbors
  k-nearest neighbor classification
  
• Support Vector Machine Classification
  Support vector machines for binary or multiclass classification
  
• Classification Ensembles
  Boosting, random forest, bagging, random subspace, and ECOC ensembles for multiclass learning
  
• Generalized Additive Model
  Interpretable model composed of univariate and bivariate shape functions for binary classification
  
• Neural Networks
  Neural networks for binary and multiclass classification
  
• Incremental Learning
  Fit classification model to streaming data and track its performance
  
• Semi-Supervised Learning for Classification
  Graph-based and self-training methods for semi-supervised learning
  
• Fairness in Binary Classification
  Explore fairness in binary classification
  
• Interpretability
  Train interpretable classification models and interpret complex classification models
  
• Model Building and Assessment
  Feature selection, feature engineering, model selection, hyperparameter optimization, cross-validation, predictive performance evaluation, and classification accuracy comparison tests