Bias vs variance: high bias = underfitting, high variance = overfitting

Boosting (machine learning)

Boosting reduces bias in ML models

to standardize: when you need zero mean and unit variance for gradient-based optimization

Standardize when zero mean and unit variance are required for gradient-based optimization

L1 vs L2 regularization: L1 gives sparsity (feature selection), L2 gives small weights

L1 regularization: L1 = L2 + sparsity; L2 regularization: L2 = L1 + small weights

log-loss / cross-entropy loss penalizes: confident wrong predictions more heavily

Log-loss penalizes confident incorrect predictions more heavily

Effect size

Cohen's D benchmarks: 0.2 = small, 0.5 = medium, 0.8 = large effect

Race and intelligence

IQ test performance differences between racial groups have decreased over time