Sklearn provides metrics that help us evaluate our models with multiple formulas. The three main metrics used to evaluate models are mean absolute error, mean squared error, and R²score.

Let's quickly try these methods:

# Scoring the model
from sklearn.metrics import r2_score, mean_squared_error,mean_absolute_error

# R2 Score
print(f"R2 score: {r2_score(y_test, y_pred)}")

# Mean Absolute Error (MAE)
print(f"MSE score: {mean_absolute_error(y_test, y_pred)}")

# Mean Squared Error (MSE)
print(f"MSE score: {mean_squared_error(y_test, y_pred)}")

The output of the preceding code is as follows:

R2 score: 0.5383003344910231
MSE score: 4.750294229575126
MSE score: 45.0773394247183

Note that we are not evaluating the accuracies we got in the preceding output. In any machine learning scenario, we try to improve accuracy by performing several optimization techniques.