Inspección de los coeficientes más fuertes

One joy of logistic regression is transparency. Its learned coefficients reveal exactly which words push a document toward each class. 🔍

After fitting, the weights sit in clf.coef_, one number per vocabulary word. That array is the heart of the model's reasoning.

The matching words live in the vectorizer. Calling get_feature_names_out gives the term for every coefficient position.

Zip the two arrays together so each word sits beside its weight. Now you can sort and read the story the model learned.

A large positive coefficient means that word strongly votes for the positive class, like words signaling spam in a spam detector.

A strongly negative coefficient pulls toward the other class. These words are the clearest evidence against the positive label.

Sort pairs by weight to surface the most influential terms. The extremes at both ends are the words that truly drive predictions.

Print the five biggest and five smallest weights. This tiny summary often reveals what your model really pays attention to.

Do the top words make sense? If a sentiment model loves the word fantastic, that intuition confirms it learned something real.

Weird top words can expose leakage, like a stray ID token, or unwanted bias. Inspecting coefficients catches these before they ship.

The further a weight is from zero, the more influence that word has. Coefficients near zero barely affect any prediction.

What does a large positive coefficient tell you?

Pair words from the vectorizer with values in coef_, then sort. The biggest coefficients show what your model learned and expose bugs early. ✅