What is precision recall tradeoff ?

Tradeoff means increasing one parameter would lead to decreasing of other. Let us explain this in context to binary classification and first define what is precision and recall. Let us call one class as positive and other as negative. Then,  TP represents the true positives, which is the number of positive predictions which are actually positive….

Machine Learning Evaluation Metrics

The purpose of any Machine Learning algorithm is to predict right value/class for the unseen data. This is called generalization and ensuring this, in general, can be very tricky. This can depend on the algorithm being used for both supervised and unsupervised learning tasks. There are two things to consider in this process – the…

What are evaluation metrics for multi-class classification problem ?

Multi-class classification evaluation can be done in either of the following ways : Average precision of each class, average recall of each class treating classifier for each class as one vs all classifier. Average of accuracy of each class treating the classifier for each class as a one-vs-all classifier Sum of all true positive entries…

What would you care more about – precision or recall for spam filtering problem?

To understand precision and recall, it is important to know about False positives(FP) : mail was NOT a SPAM but it WAS LABELLED as spam False negatives(FN): mail WAS a SPAM but was NOT LABELLED as spam True positives(TP): mail WAS a SPAM and also LABELLED as spam True negatives(TN): mail was NOT a SPAM and also LABELLED as…

How can you increase the recall of a search query (on search engine or e-commerce site) result without changing the algorithm ?

Since we are not allowed to change the algorithm, we can only play with modifying or augmenting the search query. (Note, we either change the algorithm/model or the data, here we can only change the data, in other words modifying the search query.) Modifying the query in a way that we get results relevant to…