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# Random Forest Classifier Example

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import plot_confusion_matrix

import matplotlib.pyplot as plt

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()