mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-18 01:00:15 +00:00
46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
# 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()
|