tra_analysis.RandomForest

RandomForest is a class that holds 2 submodules. The submodules are random forest classification and regression models.

Note: After update 3.x, the classness of this module has been removed.

tra_analysis.RandomForest.random_forest_classifier(self, data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None)

Uses a random forest model for classification of data. Expects inputs as data and lables. Refer to sklearn's documentation for more information on use.

Given data and labels, it returns a model, confusion matrix (cm), and classification report (cr).

Example Usage

from tra_analysis import RandomForest

data = [[0], [1], [2], [3]]

labels = [0, 0, 1, 1]

RandomForest.random_forest_classifier(data, labels, test_size = 0.25, n_estimators = 2)[0]
RandomForest.random_forest_classifier(data, labels, test_size = 0.25, n_estimators = 2)[1][0]
RandomForest.random_forest_classifier(data, labels, test_size = 0.25, n_estimators = 2)[1][1]

outputs:

[0] (model)

RandomForestClassifier(n_estimators=2)

[1][0] (cm)

[[1]]

[1][1] (cr)

              precision    recall  f1-score   support

           1       1.00      1.00      1.00         1

    accuracy                           1.00         1
   macro avg       1.00      1.00      1.00         1
weighted avg       1.00      1.00      1.00         1

tra_analysis.RandomForest.random_forest_regressor(self, data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False)

Uses a random forest model for regression of data. Expects inputs as data and outputs. Refer to sklearn's documentation for more information on use.

Given data and outputs, it returns a model, r², mean squared error (mse), and root mean squared (rms).

Example Usage

from tra_analysis import RandomForest

x = [[1.2], [3.4], [0.6], [7.8], [4.3], [8.9]]
y = [0.3, 5.2, 9.8, 1.6, 5.0, 7.6]

RandomForest().random_forest_regressor(x, y, test_size = 0.25, n_estimators = 2)[0]
RandomForest().random_forest_regressor(x, y, test_size = 0.25, n_estimators = 2)[1][0]
RandomForest().random_forest_regressor(x, y, test_size = 0.25, n_estimators = 2)[1][1]
RandomForest().random_forest_regressor(x, y, test_size = 0.25, n_estimators = 2)[1][2]

outputs:

[0] (model)

RandomForestRegressor(n_estimators=2)

[1][0] (r²)

-7.534026465028354

[1][1] (mse)

22.930000000000007

[1][2] (rms)

5.367727638395972