MAP (mean average precision) measures: area under the precision-recall curve averaged across queries

BLEU vs ROUGE: BLEU measures precision of n-grams, ROUGE measures recall

BLEU measures precision of n-grams, ROUGE measures recall

Maximum a posteriori estimation

MAP estimation incorporates a prior P(θ)

to normalize features: when features have different scales and you use distance-based methods

Normalize features when they have different scales for distance-based methods

Precision and recall

Precision = Relevant retrieved instances / All retrieved instances

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

High bias = underfitting, high variance = overfitting

Euclidean geometry

Euclidean distance measures absolute position in space