What is F-measure in information retrieval?

Assume an information retrieval (IR) system has recall R and precision P on a test document collection and an information need. The F-measure of the system is defined as the weighted harmonic mean of its precision and recall, that is, F = {1\over \alpha {1\over P}+(1-\alpha ) {1\over R}}, where the weight α ∈ [0,1].

What does F-measure measure?

In statistical analysis of binary classification, the F-score or F-measure is a measure of a test’s accuracy.

What is F-measure in confusion matrix?

Precision quantifies the number of positive class predictions that actually belong to the positive class. Recall quantifies the number of positive class predictions made out of all positive examples in the dataset. F-Measure provides a single score that balances both the concerns of precision and recall in one number.

How do you measure retrieval effectiveness?

The most well known pair of variables jointly measuring retrieval effectiveness are precision and recall, precision being the proportion of the retrieved documents that are relevant, and recall being the proportion of the relevant documents that have been retrieved.

What is a good f measure?

This is the harmonic mean of the two fractions. The result is a value between 0.0 for the worst F-measure and 1.0 for a perfect F-measure. The intuition for F-measure is that both measures are balanced in importance and that only a good precision and good recall together result in a good F-measure.

What is Bpref?

Bpref is a preference-based information retrieval measure that considers whether relevant documents are ranked above irrelevant ones.

What is a good F1-score?

Following our Data Science principles, I came up with a simple first version optimizing for its F1 score , the most-recommended quality measure for such a binary classification problem. Clearly, the higher the F1 score the better, with 0 being the worst possible and 1 being the best.

What is F-score in feature importance?

In other words, F-score reveals the discriminative power of each feature independently from others. One score is computed for the first feature, and another score is computed for the second feature. But it does not indicate anything on the combination of both features (mutual information).

How do you calculate precision in information retrieval?

Precision = Total number of documents retrieved that are relevant/Total number of documents that are retrieved.

What does a high F score mean?

If you get a large f value (one that is bigger than the F critical value found in a table), it means something is significant, while a small p value means all your results are significant. The F statistic just compares the joint effect of all the variables together.

Is Higher F1 score better?

In the most simple terms, higher F1 scores are generally better. Recall that F1 scores can range from 0 to 1, with 1 representing a model that perfectly classifies each observation into the correct class and 0 representing a model that is unable to classify any observation into the correct class.

What is interpolated precision?

Interpolated precision is where you pick a recall level r and for all recall levels P(r’) >= P(r), where P(r) is the precision at rank r . It is the best precision you can achieve.

What is the F score in psychology?

The F score is defined as the weighted harmonic mean of the test’s precision and recall. This score is calculated according to: with the precision and recall of a test taken into account. Precision, also called the positive predictive value, is the proportion of positive results that truly are positive.

How do you calculate f measure with precision and recall?

Once precision and recall have been calculated for a binary or multiclass classification problem, the two scores can be combined into the calculation of the F-Measure. The traditional F measure is calculated as follows: F-Measure = (2 * Precision * Recall) / (Precision + Recall) This is the harmonic mean of the two fractions.

How do you calculate the F measure in statistics?

The traditional F measure is calculated as follows: F-Measure = (2 * Precision * Recall) / (Precision + Recall) This is the harmonic mean of the two fractions. This is sometimes called the F-Score or the F1-Score and might be the most common metric used on imbalanced classification problems.

What is the best F measure for imbalanced data?

… the F1-measure, which weights precision and recall equally, is the variant most often used when learning from imbalanced data. — Page 27, Imbalanced Learning: Foundations, Algorithms, and Applications, 2013. Like precision and recall, a poor F-Measure score is 0.0 and a best or perfect F-Measure score is 1.0