fix(metrics): correct explained_variance residual-variance term

[teddytennant]

What

SingleTargetRegression::explained_variance computed the residual-variance numerator as diff.mapv_into(|x| x * x).sum() - mean_error, i.e. Σ(diffᵢ²) − mean(diff). That subtracts the raw residual mean (units of y) from a sum of squared residuals (units of y²), which is dimensionally inconsistent.

Explained variance is defined as:

EV = 1 − Var(y_true − y_pred) / Var(y_true)

so the numerator must be the sum of squared deviations of the residuals from their mean, Σ(diffᵢ − mean_error)². mean_error = diff.mean() was already computed but misused. This PR uses it correctly. Numerator and denominator are both sums over the same n samples, so the biased-variance 1/n factors cancel and the existing + 1e-10 denominator guard is preserved.

Why

For y_true = [0, 0.1, 0.2, 0.3, 0.4], y_pred = [0.1, 0.3, 0.2, 0.5, 0.7]:

quantity	value
old explained_variance	0.8 (matches neither R² nor EV)
r2	−0.8
corrected explained_variance	0.48
sklearn.metrics.explained_variance_score	0.48

The old result of 0.8 corresponded to no standard metric. The corrected value matches scikit-learn's reference implementation.

Testing

• Updated test_explained_variance_for_single_targets to assert the corrected value 0.48 (was 0.8), with a comment citing the scikit-learn reference.
• test_same (explained_variance(ones, ones) == 1.0) still passes: diff == 0 ⇒ numerator 0 ⇒ 1 − 0/1e-10 = 1.0.
• cargo test -p linfa explained_variance and the full cargo test -p linfa --lib metrics_regression suite (16 tests) pass.
• cargo fmt --check and cargo clippy -p linfa --lib --tests -- -D warnings are clean.

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