Fitting and evaluating an ICR Random Forest

In this example we fit and evaluate an icrlearn.ICRRandomForestClassifier on the Iris dataset.

Load the Iris dataset and split it into training and test sets

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

Train an ICRRandomForestClassifier on the training set

from icrlearn import ICRRandomForestClassifier

clf = ICRRandomForestClassifier().fit(X_train, y_train)

Evaluate the classifier on the test set and print some metrics

from sklearn.metrics import classification_report

y_pred = clf.predict(X_test)
classification_report = classification_report(y_test, y_pred)
print(classification_report)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        10
           1       0.73      1.00      0.84         8
           2       1.00      0.75      0.86        12

    accuracy                           0.90        30
   macro avg       0.91      0.92      0.90        30
weighted avg       0.93      0.90      0.90        30

Plot a confusion matrix of the classifier’s predictions

import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix

conf_matr = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_matr, annot=True)
plt.xlabel("Predicted")
plt.ylabel("True")
plt.show()