Precision = Relevant retrieved instances / All retrieved instances
Precision and recall
Precision = Relevant retrieved instances / All retrieved instances
Precision measures the accuracy of positive predictions. It is calculated by dividing the number of relevant instances retrieved by the total number of instances retrieved. This metric helps to understand the proportion of positive identifications that were actually correct.
Example
If a search engine retrieves 100 documents and 80 of them are relevant, the precision would be 80/100 = 0.8 or 80%.
Precision is crucial for evaluating the performance of classification models and information retrieval systems, ensuring that the predictions made are reliable and trustworthy.
Related concepts
BLEU
BLEU = exp(Σ(w_t * log(p_t)))
TF-IDF scoring
TF-IDF = (Term Frequency) * (Inverse Document Frequency)
Mean squared error
Mean squared error (MSE) formula: MSE = (1/n) * Σ(y_i - ŷ_i)²
Expected value
Expected value formula: E[X] = Σ [x * P(x)]
Standard deviation
Standard deviation (σ) is the square root of variance
Batch normalization
Batch normalization formula: Y = (X - μ) / σ * γ + β
One email a day: 5 concepts + the 5 stories that matter →
Swipe through 100 ML concepts daily
Open TickerNews