random_forest_classifier.py

# Random Forest Classifier Example
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split


def main():

 """
 Random Forest Classifier Example using sklearn function.
 Iris type dataset is used to demonstrate algorithm.
 """

 # Load Iris dataset
 iris = load_iris()

 # Split dataset into train and test data
 x = iris["data"] # features
 y = iris["target"]
 x_train, x_test, y_train, y_test = train_test_split(
 x, y, test_size=0.3, random_state=1
 )

 # Random Forest Classifier
 rand_for = RandomForestClassifier(random_state=42, n_estimators=100)
 rand_for.fit(x_train, y_train)

 # Display Confusion Matrix of Classifier
 plot_confusion_matrix(
 rand_for,
 x_test,
 y_test,
 display_labels=iris["target_names"],
 cmap="Blues",
 normalize="true",
 )
 plt.title("Normalized Confusion Matrix - IRIS Dataset")
 plt.show()


if __name__ == "__main__":
 main()