Machine Learning with Scikit-Learn

python
Run Inline
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
# Load data
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)
# Preprocess
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
# Hyperparameter tuning
param_grid = {'n_estimators': [50, 100, 200], 'max_depth': [3, 5, None]}
grid = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)
grid.fit(X_train, y_train)
print(f"Best params: {grid.best_params_}")