Prediction Class

The prediction class is used for customizing how a tree.predict functions. The defaults can be seen below.

Predict

Predict(X, Y, root)

The base Predict class from which all other predict classes need to inhert.

forest_predict staticmethod

forest_predict(X_old, Y_old, X_new, trees, parallel, **kwargs)

Internal function used by the RandomForest class 'predict' method to evaluate predictions for each tree and aggregate them.

Needs to be adjusted whenever RandomForest predictions do not simply aggregate the tree predictions by averaging.

Parameters:

Name	Type	Description	Default
X_old	ndarray	Array of feature values used during training.	required
Y_old	ndarray	Array of response values used during training.	required
X_new	ndarray	Array of new feature values at which to predict.	required
tree		List of fitted DecisionTrees fitted within the random forest.	required
parallel	ParallelModel	ParallelModel used for multiprocessing.	required

Returns:

Type	Description
ndarray	An array with predictions for each row of X_new.

predict

predict(X, **kwargs)

Prediction function called by the DecisionTree when it is told to predict. Can be customised for a different output of the DecisionTree.

Parameters:

Name	Type	Description	Default
X	ndarray	A 2-dimensional array for which the rows are the samples at which to predict.	required

Returns:

Type	Description
ndarray	An array with predictions for each row of X.

predict_leaf

predict_leaf(X)

Computes hash table indexing in which LeafNodes the rows of X fall into.

Parameters:

Name	Type	Description	Default
X	ndarray	2-dimensional array for which the rows are the samples at which to predict.	required

Returns:

Type	Description
dict	A hash table with keys corresponding to LeafNode ids and values lists of indices specifying which rows land in a given LeafNode.

PredictClassification

PredictClassification(X, Y, root)

The default prediction class for the 'Classification' tree type.

predict

predict(X, **kwargs)

For each row in X, the method first predicts the LeafNode into which the row falls and then computes the class with the highest number of occurances in that LeafNode.

If the keyword argument 'predict_proba' is set to True. This method outputs class probabilities (i.e., the frequence of each label in the same LeafNode).

Parameters:

Name	Type	Description	Default
X	ndarray	A 2-dimensional array for which the rows are the samples at which to predict.	required
**kwargs		predict_proba : bool Specifies whether to compute class probabilities or not. Defaults to False if not provided.	{}

PredictRegression

PredictRegression(X, Y, root)

The default prediction class for the 'Regression' tree type.

predict

predict(X, **kwargs)

For each row in X, the method first predicts the LeafNode into which the row falls and then computes average response value in that LeafNode.

Parameters:

Name	Type	Description	Default
X	ndarray	A 2-dimensional array for which the rows are the samples at which to predict.	required

Returns:

Type	Description
ndarray	An array with predictions for each row of X.