rr.experiment.algorithm module¶

class rr.experiment.algorithm.Model(nb_tree_per_forest=50, max_depth=10)¶

Bases: object

Create a new ML model (Random forest classifier from scikitlearn)

Parameters

nb_tree_per_forest (int) – number of decision trees in the forest
max_depth (int) – max depth of the trees

Returns

None

Raises

None –

predict(X)¶

Make a prediction on the data using the trained model

Parameters: X (numpy.ndarray) – A NxM 2D-array where each row corresponds to a sample and each column to a feature
Returns: A 1D array (with a dtype of int) containing the predicted label for each sample
Return type: numpy.ndarray
Raises: None –

train(X, y)¶

Train the model using the given data

Parameters

X (numpy.ndarray) – A NxM 2D-array where each row corresponds to a sample and each column to a feature
y (numpy.ndarray) – A 1D-array of length N, where each element corresponds to a sample label

Returns

None

Raises

None –