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# ml. Machine Learning

The Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data.

Most of the classification and regression algorithms are implemented as C++ classes. As the algorithms have different sets of features (like an ability to handle missing measurements or categorical input variables), there is a little common ground between the classes. This common ground is defined by the class CvStatModel that all the other ML classes are derived from.

- Statistical Models
  - StatModel
  - StatModel::train
  - StatModel::isTrained
  - StatModel::isClassifier
  - StatModel::getVarCount
  - StatModel::predict
  - StatModel::calcError
  - StatModel::save
  - StatModel::load
- Normal Bayes Classifier
  - NormalBayesClassifier
  - NormalBayesClassifier::create
  - NormalBayesClassifier::predictProb
- K-Nearest Neighbors
  - KNearest
  - KNearest::create
  - KNearest::findNearest
  - KNearest::getDefaultK
  - KNearest::setDefaultK
- Support Vector Machines
  - ParamGrid
  - ParamGrid::ParamGrid
  - SVM::Params
  - SVM::Params::Params
  - SVM
  - SVM::create
  - SVM::trainAuto
  - SVM::getDefaulltGrid
  - SVM::getParams
  - SVM::getSupportVectors
  - SVM::getDecisionFunction
  - Prediction with SVM
- Decision Trees
  - Predicting with Decision Trees
  - Training Decision Trees
  - Variable Importance
  - DTrees::Split
  - DTrees::Node
  - DTrees::Params
  - DTrees::Params::Params
  - DTrees
  - DTrees::create
  - DTrees::getDParams
  - DTrees::setDParams
  - DTrees::getRoots
  - DTrees::getNodes
  - DTrees::getSplits
  - DTrees::getSubsets
- Boosting
  - Boost::Params
  - Boost::Params::Params
  - Boost
  - Boost::create
  - Boost::getBParams
  - Boost::setBParams
  - Prediction with Boost
- Random Trees
  - RTrees::Params
  - RTrees::Params::Params
  - RTrees
  - RTrees::create
  - RTrees::getVarImportance
- Expectation Maximization
  - EM
  - EM::Params
  - EM::Params::Params
  - EM::create
  - EM::train
  - EM::predict2
  - EM::getMeans
  - EM::getWeights
  - EM::getCovs
- Neural Networks
  - ANN_MLP::Params
  - ANN_MLP::Params::Params
  - ANN_MLP
  - ANN_MLP::create
  - ANN_MLP::setParams
  - ANN_MLP::getParams
- Logistic Regression
  - LogisticRegression::Params
  - LogisticRegression::Params::Params
  - LogisticRegression
  - LogisticRegression::create
  - LogisticRegression::train
  - LogisticRegression::predict
  - LogisticRegression::get_learnt_thetas
  - LogisticRegression::read
  - LogisticRegression::write
- Training Data
  - TrainData
  - TrainData::loadFromCSV
  - TrainData::create
  - TrainData::getTrainSamples
  - TrainData::getTrainResponses
  - TrainData::getClassLabels
  - TrainData::getTrainNormCatResponses
  - TrainData::setTrainTestSplitRatio
  - Other methods

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