• C++ API Documentation

  • Quickstart
  • Installing mlpack
  • Cross-compilation setup
  • Data representation in mlpack
  • Citation
  • Tutorials and Examples

    • mlpack examples repository
    • mlpack Youtube channel
    • mlpack models repository
  • Utility classes

    • Math
    • Distances
    • Distributions
    • Kernels
    • Trees

      • KDTree
      • MeanSplitKDTree
      • BallTree
      • MeanSplitBallTree
      • VPTree
      • RPTree
      • MaxRPTree
      • UBTree
      • BinarySpaceTree
      • CoverTree
      • Octree
      • RTree
      • RStarTree
      • XTree
      • RPlusTree
      • RPlusPlusTree
      • HilbertRTree
      • RectangleTree
  • Data loading and I/O

    • Numeric data
    • Mixed categorical data
    • Image data
    • mlpack models and objects
  • Preprocessing/feature extraction

    • Normalizing labels
    • Dataset splitting
  • Transformations

    • AMF
    • LocalCoordinateCoding
    • LMNN
    • NCA
    • NMF
    • PCA
    • RADICAL
    • SparseCoding
  • Modeling

    • Classification

      • AdaBoost
      • DecisionTree
      • HoeffdingTree
      • LinearSVM
      • LogisticRegression
      • NaiveBayesClassifier
      • Perceptron
      • RandomForest
      • SoftmaxRegression
    • Regression

      • BayesianLinearRegression
      • DecisionTreeRegressor
      • LARS
      • LinearRegression
    • Clustering

      • MeanShift
    • Geometric algorithms
  • Evaluation

    • Cross-validation
    • Hyperparameter tuning
  • Deployment

    • Compilation
    • Cross-compile to RPi2
    • Deploying mlpack on Windows
    • Deploying with Docker
  • Developers

    • Community
    • GSoC
    • CI/CD
    • Timers
    • Binding system
    • Writing a binding
    • Template policies

      • ElemType
      • DistanceType
      • KernelType
      • TreeType

• Binding API

  • Python quickstart
  • Python binding documentation
  • Julia quickstart
  • Julia binding documentation
  • R quickstart
  • R binding documentation
  • CLI quickstart
  • CLI binding documentation
  • Go quickstart
  • Go binding documentation

• Evaluation [top]

Evaluation

Once a model is trained, mlpack contains a number of utilities for testing the model.

• Cross-validation: k-fold cross-validation tools for any mlpack algorithm
• Hyperparameter tuning: generic hyperparameter tuner to find good hyperparameters for any mlpack algorithm