Open carmelo-cyber opened 1 year ago
from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)
clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test) print(f"Model accuracy: {accuracy:.2f}")
new_data = [[5.1, 3.5, 1.4, 0.2], [6.3, 3.3, 4.7, 1.6]] predictions = clf.predict(new_data) print(f"Predictions: {predictions}")
Import necessary libraries
from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier
Load the iris dataset
iris = datasets.load_iris()
Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)
Train a random forest classifier on the training data
clf = RandomForestClassifier(n_estimators=100) clf.fit(X_train, y_train)
Evaluate the classifier on the test data
accuracy = clf.score(X_test, y_test) print(f"Model accuracy: {accuracy:.2f}")
Use the classifier to make predictions on new data
new_data = [[5.1, 3.5, 1.4, 0.2], [6.3, 3.3, 4.7, 1.6]] predictions = clf.predict(new_data) print(f"Predictions: {predictions}")