what is the difference between transformer and estimator in sklearn?

Question

I saw both transformer and estimator were mentioned in the sklearn documentation.

Is there any difference between these two words?

Answer 1

The basic difference is that a:

• Transformer transforms the input data (X) in some ways.
• Estimator predicts a new value (or values) (y) by using the input data (X).

Both the Transformer and Estimator should have a fit() method which can be used to train them (they learn some characteristics of the data). The signature is:

fit(X, y)

fit() does not return any value, just stores the learnt data inside the object.

Here X represents the samples (feature vectors) and y is the target vector (which may have single or multiple values per corresponding sample in X). Note that y can be optional in some transformers where its not needed, but its mandatory for most estimators (supervised estimators). Look at StandardScaler for example. It needs the initial data X for finding the mean and std of the data (it learns the characteristics of X, y is not needed).

Each Transformer should have a transform(X, y) function which like fit() takes the input X and returns a new transformed version of X (which generally should have same number samples but may or may not have same features).

On the other hand, Estimator should have a predict(X) method which should output the predicted value of y from the given X.

There will be some classes in scikit-learn which implement both transform() and predict(), like KMeans, in that case carefully reading the documentation should solve your doubts.