Bias-variance tradeoff
Intermediate classical-mlevaluationmodel-complexity
Implement:
def polynomial_features(x, degree) -> np.ndarray: ...
def fit_polynomial(x, y, degree) -> np.ndarray: ...
def predict_polynomial(coefficients, x) -> np.ndarray: ...
def bias_variance_decomposition(predictions, targets) -> tuple[float, float, float]: ...
predictions in bias_variance_decomposition is many independently-trained models' predictions on the same test points, one row per model, targets is the true, noiseless function value at each test point (not a noisy training label).