Fitting data vs. transforming data in scikit-learn

Question

In scikit-learn, all estimators have a fit() method, and depending on whether they are supervised or unsupervised, they also have a predict() or transform() method.

I am in the process of writing a transformer for an unsupervised learning task and was wondering if there is a rule of thumb where to put which kind of learning logic. The official documentation is not very helpful in this regard:

fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.

In this context, what is meant by both fitting data and transforming data?

Answer 1

Fitting finds the internal parameters of a model that will be used to transform data. Transforming applies the parameters to data. You may fit a model to one set of data, and then transform it on a completely different set.

For example, you fit a linear model to data to get a slope and intercept. Then you use those parameters to transform (i.e., map) new or existing values of x to y.

fit_transform is just doing both steps to the same data.

A scikit example: You fit data to find the principal components. Then you transform your data to see how it maps onto these components:

from sklearn.decomposition import PCA  pca = PCA(n_components=2)  X = [[1,2],[2,4],[1,3]]  pca.fit(X)  # This is the model to map data pca.components_  array([[ 0.47185791,  0.88167459],        [-0.88167459,  0.47185791]], dtype=float32)  # Now we actually map the data pca.transform(X)  array([[-1.03896057, -0.17796634],        [ 1.19624651, -0.11592512],        [-0.15728599,  0.29389156]])  # Or we can do both "at once" pca.fit_transform(X)  array([[-1.03896058, -0.1779664 ],        [ 1.19624662, -0.11592512],        [-0.15728603,  0.29389152]], dtype=float32)