multi_regressor_SVM.py

from sklearn.datasets import make_regression
from sklearn.multioutput import MultiOutputRegressor
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error

# Generate dataset
X, y = make_regression(n_samples=25000, n_features=3, n_targets=2, random_state=33)

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=33)

# Create the SVR regressor
svr = SVR(epsilon=0.2)

# Create the Multioutput Regressor
mor = MultiOutputRegressor(svr)

# Train the regressor
mor = mor.fit(X_train, y_train)

# Generate predictions for testing data
y_pred = mor.predict(X_test)

# Evaluate the regressor
mse_one = mean_squared_error(y_test[:,0], y_pred[:,0])
mse_two = mean_squared_error(y_test[:,1], y_pred[:,1])
print(f'MSE for first regressor: {mse_one} - second regressor: {mse_two}')
mae_one = mean_absolute_error(y_test[:,0], y_pred[:,0])
mae_two = mean_absolute_error(y_test[:,1], y_pred[:,1])
print(f'MAE for first regressor: {mae_one} - second regressor: {mae_two}')