Problem_11: Implement a random forest classifier using Scikit-learn
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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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