Learning

Learn(table; [model])

Perform supervised learning with labeled table and statistical learning model.

Uses KNNClassifier(1) or KNNRegressor(1) model by default depending on the scientific type of the labels stored in the table.

Examples

Learn(label(table, "y"))
Learn(label(table, ["y1", "y2"]))
Learn(label(table, 3), model=KNNClassifier(5))

See also label.

label(table, names)

Creates a LabeledTable from table using names as label columns.

KNNClassifier(k, metric=Euclidean(); leafsize=10, reorder=true)

K-nearest neighbor classification model with k neighbors and metric from Distances.jl. Optionally, specify the leafsize and reorder options for the underlying trees in NearestNeighbors.jl.

See also KNNRegressor.

KNNRegressor(k, metric=Euclidean(); leafsize=10, reorder=true)

K-nearest neighbor regression model with k neighbors and metric from Distances.jl. Optionally, specify the leafsize and reorder options for the underlying trees in NearestNeighbors.jl.

See also KNNClassifier.

LinearRegressor(; kwargs...)

Linear regression model.

The kwargs are forwarded to the GLM.lm function from GLM.jl.

See also GeneralizedLinearRegressor.

GeneralizedLinearRegressor(dist, link; kwargs...)

Generalized linear regression model with distribution dist from Distributions.jl and link function.

The kwargs are forwarded to the GLM.glm function from GLM.jl.

See also LinearRegressor.

DecisionTreeClassifier(; pruning_purity_threshold=0.0,
                       max_depth::Int=-1,
                       min_samples_leaf::Int=1,
                       min_samples_split::Int=2,
                       min_purity_increase::Float=0.0,
                       n_subfeatures::Int=0,
                       rng=Random.GLOBAL_RNG,
                       impurity_importance::Bool=true)

Decision tree classifier. See DecisionTree.jl's documentation

Hyperparameters:

• pruning_purity_threshold: (post-pruning) merge leaves having >=thresh combined purity (default: no pruning)
• max_depth: maximum depth of the decision tree (default: no maximum)
• min_samples_leaf: the minimum number of samples each leaf needs to have (default: 1)
• min_samples_split: the minimum number of samples in needed for a split (default: 2)
• min_purity_increase: minimum purity needed for a split (default: 0.0)
• n_subfeatures: number of features to select at random (default: keep all)
• rng: the random number generator to use. Can be an Int, which will be used to seed and create a new random number generator.
• impurity_importance: whether to calculate feature importances using Mean Decrease in Impurity (MDI). See DecisionTree.impurity_importance

Implements fit!, predict, predict_proba, get_classes

DecisionTreeRegressor(; pruning_purity_threshold=0.0,
                      max_depth::Int-1,
                      min_samples_leaf::Int=5,
                      min_samples_split::Int=2,
                      min_purity_increase::Float=0.0,
                      n_subfeatures::Int=0,
                      rng=Random.GLOBAL_RNG,
                      impurity_importance::Bool=true)

Decision tree regression. See DecisionTree.jl's documentation

Hyperparameters:

• pruning_purity_threshold: (post-pruning) merge leaves having >=thresh combined purity (default: no pruning). This accuracy-based method may not be appropriate for regression tree.
• max_depth: maximum depth of the decision tree (default: no maximum)
• min_samples_leaf: the minimum number of samples each leaf needs to have (default: 5)
• min_samples_split: the minimum number of samples in needed for a split (default: 2)
• min_purity_increase: minimum purity needed for a split (default: 0.0)
• n_subfeatures: number of features to select at random (default: keep all)
• rng: the random number generator to use. Can be an Int, which will be used to seed and create a new random number generator.
• impurity_importance: whether to calculate feature importances using Mean Decrease in Impurity (MDI). See DecisionTree.impurity_importance

Implements fit!, predict, get_classes

RandomForestClassifier(; n_subfeatures::Int=-1,
                       n_trees::Int=10,
                       partial_sampling::Float=0.7,
                       max_depth::Int=-1,
                       rng=Random.GLOBAL_RNG,
                       impurity_importance::Bool=true)

Random forest classification. See DecisionTree.jl's documentation

Hyperparameters:

• n_subfeatures: number of features to consider at random per split (default: -1, sqrt(# features))
• n_trees: number of trees to train (default: 10)
• partial_sampling: fraction of samples to train each tree on (default: 0.7)
• max_depth: maximum depth of the decision trees (default: no maximum)
• min_samples_leaf: the minimum number of samples each leaf needs to have
• min_samples_split: the minimum number of samples in needed for a split
• min_purity_increase: minimum purity needed for a split
• rng: the random number generator to use. Can be an Int, which will be used to seed and create a new random number generator. Multi-threaded forests must be seeded with an Int
• impurity_importance: whether to calculate feature importances using Mean Decrease in Impurity (MDI). See DecisionTree.impurity_importance

Implements fit!, predict, predict_proba, get_classes

RandomForestRegressor(; n_subfeatures::Int=-1,
                      n_trees::Int=10,
                      partial_sampling::Float=0.7,
                      max_depth::Int=-1,
                      min_samples_leaf::Int=5,
                      rng=Random.GLOBAL_RNG,
                      impurity_importance::Bool=true)

Random forest regression. See DecisionTree.jl's documentation

Hyperparameters:

• n_subfeatures: number of features to consider at random per split (default: -1, sqrt(# features))
• n_trees: number of trees to train (default: 10)
• partial_sampling: fraction of samples to train each tree on (default: 0.7)
• max_depth: maximum depth of the decision trees (default: no maximum)
• min_samples_leaf: the minimum number of samples each leaf needs to have (default: 5)
• min_samples_split: the minimum number of samples in needed for a split
• min_purity_increase: minimum purity needed for a split
• rng: the random number generator to use. Can be an Int, which will be used to seed and create a new random number generator. Multi-threaded forests must be seeded with an Int
• impurity_importance: whether to calculate feature importances using Mean Decrease in Impurity (MDI). See DecisionTree.impurity_importance.

Implements fit!, predict, get_classes

AdaBoostStumpClassifier(; n_iterations::Int=10,
                        rng=Random.GLOBAL_RNG)

Adaboosted decision tree stumps. See DecisionTree.jl's documentation

Hyperparameters:

• n_iterations: number of iterations of AdaBoost
• rng: the random number generator to use. Can be an Int, which will be used to seed and create a new random number generator.

Implements fit!, predict, predict_proba, get_classes