Problem_11: Implement a random forest classifier using Scikit-learn

Publicado em: 01 Janeiro 1970
no canal de: Upgrade2python

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Problem_11:
Implement a random forest classifier using Scikit-learn
#pythoncode :

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

Load dataset
iris = load_iris()
X = iris.data
y = iris.target

Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train random forest classifier
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)

Predict and evaluate
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print(f'Accuracy: {accuracy}')

Explanation: This code trains a random forest classifier on the Iris dataset, splits the data, and evaluates the model's accuracy.

#sample_output :

Accuracy: 1.0

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