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- builtins.object
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- RandomForestClassifier
class RandomForestClassifier(builtins.object) |
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RandomForestClassifier(forest_size=100, max_depth=10, min_samples_split=2, n_jobs=-1, random_seed=None, X=None, y=None)
RandomForestClassifier is a custom implementation of a Random Forest classifier.
Attributes:
n_estimators (int): The number of trees in the forest.
max_depth (int): The maximum depth of each tree.
n_jobs (int): The number of jobs to run in parallel. Defaults to -1 (use all available processors).
random_state (int or None): The seed for random number generation. Defaults to None.
trees (list): A list of trained decision trees.
bootstraps (list): A list of bootstrapped indices for out-of-bag (OOB) scoring.
X (numpy.ndarray or None): The feature matrix used for training.
y (numpy.ndarray or None): The target labels used for training.
accuracy (float): The accuracy of the model after fitting.
precision (float): The precision of the model after fitting.
recall (float): The recall of the model after fitting.
f1_score (float): The F1 score of the model after fitting.
log_loss (float or None): The log loss of the model after fitting (only for binary classification).
Methods:
__init__(forest_size=100, max_depth=10, n_jobs=-1, random_seed=None, X=None, y=None):
Initializes the RandomForestClassifier object with the specified parameters.
fit(X=None, y=None, verbose=False):
Fits the random forest model to the provided data using parallel processing.
calculate_metrics(y_true, y_pred):
Calculates evaluation metrics (accuracy, precision, recall, F1 score, and log loss) for classification.
predict(X):
Predicts class labels for the provided data using the trained random forest.
get_stats(verbose=False):
Returns the evaluation metrics (accuracy, precision, recall, F1 score, and log loss) as a dictionary.
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Methods defined here:
- __init__(self, forest_size=100, max_depth=10, min_samples_split=2, n_jobs=-1, random_seed=None, X=None, y=None)
- Initializes the RandomForest object.
- calculate_metrics(self, y_true, y_pred)
- Calculate evaluation metrics for classification.
- fit(self, X=None, y=None, sample_weight=None, verbose=False)
- Fit the random forest with parallel processing.
- get_params(self)
- Get the parameters of the RandomForestClassifier.
- get_stats(self, verbose=False)
- Return the evaluation metrics.
- predict(self, X)
- Predict class labels for the provided data.
- predict_proba(self, X)
- Predict class probabilities for the provided data.
Args:
X (array-like): The input features.
Returns:
np.ndarray: A 2D array where each row represents the probability distribution
over the classes for a record.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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