/// <summary>
/// Initializes a new instance of the DecisionTreeClassifier class.
/// </summary>
/// <param name="criterion">The function to measure the quality of a split. Supported criteria are
/// <see cref="Criterion.Gini"/> for the Gini impurity and <see cref="Criterion.Entropy"/>
/// for the information gain.</param>
/// <param name="splitter">The strategy used to choose the split at each node. Supported
/// strategies are <see cref="Splitter.Best"/> to choose the best split and <see cref="Splitter.Random"/> to choose
/// the best random split.</param>
/// <param name="maxDepth">The maximum depth of the tree. If <c>null</c>, then nodes are expanded until
/// all leaves are pure or until all leaves contain less than
/// <paramref name="minSamplesSplit"/> samples.</param>
/// <param name="minSamplesSplit">The minimum number of samples required to split an internal node.</param>
/// <param name="minSamplesLeaf">The minimum number of samples required to be at a leaf node.</param>
/// <param name="maxFeatures">Number of features to consider when looking for the best split. If null -
/// then all features will be considered.</param>
/// <param name="random">random number generator</param>
public DecisionTreeClassifier(
Criterion criterion = Criterion.Gini,
Splitter splitter = Splitter.Best,
int?maxDepth = null,
int minSamplesSplit = 2,
int minSamplesLeaf = 1,
MaxFeaturesChoice maxFeatures = null,
Random random = null)
: base(criterion, splitter, maxDepth, minSamplesSplit, minSamplesLeaf, maxFeatures, random)
{
}
/// <summary>
/// Initializes a new instance of the ExtraTreeRegressor class.
/// </summary>
/// <param name="criterion"> The function to measure the quality of a split. The only supported
/// criterion is <see cref="Criterion.Mse"/> for the mean squared error.</param>
/// <param name="splitter">The strategy used to choose the split at each node. Supported
/// strategies are <see cref="Splitter.Best"/> to choose the best split and <see cref="Splitter.Random"/> to choose
/// the best random split.</param>
/// <param name="maxDepth">The maximum depth of the tree. If <c>null</c>, then nodes are expanded until
/// all leaves are pure or until all leaves contain less than
/// <paramref name="minSamplesSplit"/> samples.</param>
/// <param name="minSamplesSplit">The minimum number of samples required to split an internal node.</param>
/// <param name="minSamplesLeaf">The minimum number of samples required to be at a leaf node.</param>
/// <param name="maxFeatures">Number of features to consider when looking for the best split. If null -
/// then all features will be considered.</param>
/// <param name="random">random number generator</param>
public ExtraTreeRegressor(
Criterion criterion = Criterion.Mse,
Splitter splitter = Splitter.Random,
int?maxDepth = null,
int minSamplesSplit = 2,
int minSamplesLeaf = 1,
MaxFeaturesChoice maxFeatures = null,
Random random = null) : base(criterion,
splitter,
maxDepth,
minSamplesSplit,
minSamplesLeaf,
maxFeatures ?? MaxFeaturesChoice.Auto(),
random)
{
}
/// <remarks>
/// https://github.com/scikit-learn/scikit-learn/tree/30eb78de8d1e7b25fb1a4b0d8c63afdcc972ee84/sklearn/tree/tree.py
/// </remarks>
public ExtraTreeClassifier(
Criterion criterion = Criterion.Gini,
Splitter splitter = Splitter.Random,
int?max_depth = null,
int min_samples_split = 2,
int min_samples_leaf = 1,
MaxFeaturesChoice max_features = null,
Random random_state = null) :
base(criterion,
splitter,
max_depth,
min_samples_split,
min_samples_leaf,
max_features ?? MaxFeaturesChoice.Auto(),
random_state)
{
}
/// <summary>
/// Initializes a new instance of the BaseDecisionTree class.
/// </summary>
/// <param name="criterion">The function to measure the quality of a split.</param>
/// <param name="splitter">The strategy used to choose the split at each node.</param>
/// <param name="maxDepth">
/// The maximum depth of the tree. If <c>null</c>, then nodes are expanded until
/// all leaves are pure or until all leaves contain less than
/// <paramref name="minSamplesSplit"/> samples.</param>
/// <param name="minSamplesSplit">The minimum number of samples required to split an internal node.</param>
/// <param name="minSamplesLeaf">The minimum number of samples required to be at a leaf node.</param>
/// <param name="maxFeatures">Number of features to consider when looking for the best split. If null -
/// then all features will be considered.</param>
/// <param name="randomState">random number generator</param>
public BaseDecisionTree(
Criterion criterion,
Splitter splitter,
int?maxDepth,
int minSamplesSplit,
int minSamplesLeaf,
MaxFeaturesChoice maxFeatures,
Random randomState)
{
this.criterion = criterion;
this.splitter = splitter;
this.maxDepth = maxDepth;
this.minSamplesSplit = minSamplesSplit;
this.minSamplesLeaf = minSamplesLeaf;
this.maxFeatures = maxFeatures;
this.randomState = randomState ?? new Random();
}