Fitting data vs. transforming data in scikit-learn

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)