Lesson 15: Evaluating Regression Models

Evaluating Regression Models

How do we know if our regression model is any good? For numbers, we can't just measure "accuracy" (it's almost impossible to predict exactly $312,456.78). Instead, we measure how far off our predictions are from the true values.

Common Regression Metrics

Mean Squared Error (MSE): Averages the squared differences between predictions and actuals. Heavily penalizes large errors!
Root Mean Squared Error (RMSE): The square root of MSE. Useful because it's in the same units as our target variable (e.g., dollars).
Mean Absolute Error (MAE): The average absolute difference. Easier to understand natively.
R² Score: Measures how much of the variance in the target is explained by the model. 1.0 is perfect, 0 means it's no better than guessing the average.

Python Challenge: Grade Your Model

Calculate MSE and R² score for these predictions using scikit-learn.

from sklearn.metrics import mean_squared_error, r2_score

# Actual house prices and our model's predictions (in thousands)
y_actual = [300, 450, 200, 600, 350]
y_pred = [310, 440, 210, 580, 360]

# TODO: Calculate MSE
# mse = ???

# TODO: Calculate R2 Score
# r2 = ???

# print(f"MSE: {mse}")
# print(f"R² Score: {r2}")

These metrics help us compare different models and choose the one that makes the smallest errors on our test set.