RandomForestClassifier
RandomForestClassifier
A model type for constructing a CART random forest classifier, based on DecisionTree.jl, and implementing the MLJ model interface.
From MLJ, the type can be imported using
RandomForestClassifier = @load RandomForestClassifier pkg=DecisionTree
Do model = RandomForestClassifier()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in RandomForestClassifier(max_depth=...)
.
RandomForestClassifier
implements the standard Random Forest algorithm, originally published in Breiman, L. (2001): "Random Forests.", Machine Learning, vol. 45, pp. 5–32.
Training data
In MLJ or MLJBase, bind an instance model
to data with
mach = machine(model, X, y)
where
X
: any table of input features (eg, aDataFrame
) whose columns each have one of the following element scitypes:Continuous
,Count
, or<:OrderedFactor
; check column scitypes withschema(X)
y
: the target, which can be anyAbstractVector
whose element scitype is<:OrderedFactor
or<:Multiclass
; check the scitype withscitype(y)
Train the machine with fit!(mach, rows=...)
.
Hyperparameters
max_depth=-1
: max depth of the decision tree (-1=any)min_samples_leaf=1
: min number of samples each leaf needs to havemin_samples_split=2
: min number of samples needed for a splitmin_purity_increase=0
: min purity needed for a splitn_subfeatures=-1
: number of features to select at random (0 for all, -1 for square root of number of features)n_trees=10
: number of trees to trainsampling_fraction=0.7
fraction of samples to train each tree onfeature_importance
: method to use for computing feature importances. One of(:impurity, :split)
rng=Random.GLOBAL_RNG
: random number generator or seed
Operations
predict(mach, Xnew)
: return predictions of the target given featuresXnew
having the same scitype asX
above. Predictions are probabilistic, but uncalibrated.predict_mode(mach, Xnew)
: instead return the mode of each prediction above.
Fitted parameters
The fields of fitted_params(mach)
are:
forest
: theEnsemble
object returned by the core DecisionTree.jl algorithm
Report
The fields of report(mach)
are:
features
: the names of the features encountered in training
Accessor functions
feature_importances(mach)
returns a vector of(feature::Symbol => importance)
pairs; the type of importance is determined by the hyperparameterfeature_importance
(see above)
Examples
using MLJ
Forest = @load RandomForestClassifier pkg=DecisionTree
forest = Forest(min_samples_split=6, n_subfeatures=3)
X, y = @load_iris
mach = machine(forest, X, y) |> fit!
Xnew = (sepal_length = [6.4, 7.2, 7.4],
sepal_width = [2.8, 3.0, 2.8],
petal_length = [5.6, 5.8, 6.1],
petal_width = [2.1, 1.6, 1.9],)
yhat = predict(mach, Xnew) ## probabilistic predictions
predict_mode(mach, Xnew) ## point predictions
pdf.(yhat, "virginica") ## probabilities for the "verginica" class
fitted_params(mach).forest ## raw `Ensemble` object from DecisionTrees.jl
feature_importances(mach) ## `:impurity` feature importances
forest.feature_importance = :split
feature_importance(mach) ## `:split` feature importances
See also DecisionTree.jl and the unwrapped model type MLJDecisionTreeInterface.DecisionTree.RandomForestClassifier
.