Reference 3 stops to get here
R-squared
Coefficient of determination - measures the proportion of variance in the target variable explained by the model.
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3 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Regression ✓ understood
A supervised learning task where the model predicts continuous numerical values rather than discrete categories.
- R-squared · you are here ✓ understood
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Training Mean Squared Error A loss function for regression that computes the average squared difference between predictions and targets. Evaluation Mean Absolute Error The average absolute difference between predictions and actual values, a regression metric less sensitive to outliers than MSE. Foundations Bias-Variance Tradeoff The balance between a model's bias (systematic error) and variance (sensitivity to training data fluctuations). Training Underfitting When a model is too simple to capture the underlying pattern in data, performing poorly on both training and test sets.