We have already talked about it in this post. Just want to add few more things after finishing a course. This post is just an extension of above with some practical considerations.

We are claiming that accuracy may not be a good measure always. When you are building automated machine learning you must trust it.

Case Study

• You want to show positive reviews on your website.
• Say in your dataset 90% reviews are negative.
• A classifier can achieve 90% accuracy by predicting all of them as negative.
• But what you are interested in is finding out remaining 10% and display it on your website.

Precision = Did I show something negative?

Recall = How good I am at finding positive reviews?

Analogy with Optimist and Pessimist

• Optimist assigns every/most review as positive
  • Very good recall, but less precision
• Pessimist assigns every/most review with negative
  • Bad recall, good precision

Trade-off

• Trade-off comes while scoring, not while training
• We can assign labels based on probabilities
• Decision tree gives probability by no of positive and negative samples at leaf node
• Logistic regression of-course gives probability
• We can change threshold to trade off between precision and recall
• Positive when prob > 1 => Pessimist
• Positive when prob > 0 => Optimist

Single no not always useful

• Single numbers like F1 score and AUC are something I am not great fan of
• You can not always choose classifier just by AUC, ROC curve might intersesct
  • This intersection means that one classifier is better at some range of precision
  • But if they don’t intersect we choose the one with higher AUC
• From business perspective we are should be clear whether we want more precision or recall
• Another practical metric they talked about was precision at k
  • Say I want to display 5 reviews on my website
  • What is the precision after 5 values I have chosen