what is the difference between 'transform' and 'fit_transform' in sklearn

In scikit-learn estimator api,

fit() : used for generating learning model parameters from training data

transform() : parameters generated from fit() method,applied upon model to generate transformed data set.

fit_transform() :combination of fit() and transform() api on same data set

Checkout Chapter-4 from this book & answer from stackexchange for more clarity

These methods are used to center/feature scale of a given data. It basically helps to normalize the data within a particular range

For this, we use Z-score method.

We do this on the training set of data.

1.Fit(): Method calculates the parameters μ and σ and saves them as internal objects.

2.Transform(): Method using these calculated parameters apply the transformation to a particular dataset.

3.Fit_transform(): joins the fit() and transform() method for transformation of dataset.

Code snippet for Feature Scaling/Standardisation(after train_test_split).

from sklearn.preprocessing import StandardScalersc = StandardScaler()sc.fit_transform(X_train)sc.transform(X_test)

We apply the same(training set same two parameters μ and σ (values)) parameter transformation on our testing set.

The .transform method is meant for when you have already computed PCA, i.e. if you have already called its .fit method.

In [12]: pc2 = RandomizedPCA(n_components=3)In [13]: pc2.transform(X) # can't transform because it does not know how to do it.---------------------------------------------------------------------------AttributeError                            Traceback (most recent call last)<ipython-input-13-e3b6b8ea2aff> in <module>()----> 1 pc2.transform(X)/usr/local/lib/python3.4/dist-packages/sklearn/decomposition/pca.py in transform(self, X, y)    714         # XXX remove scipy.sparse support here in 0.16    715         X = atleast2d_or_csr(X)--> 716         if self.mean_ is not None:    717             X = X - self.mean_    718 AttributeError: 'RandomizedPCA' object has no attribute 'mean_'In [14]: pc2.ftransform(X) pc2.fit            pc2.fit_transform  In [14]: pc2.fit_transform(X)Out[14]: array([[-1.38340578, -0.2935787 ],       [-2.22189802,  0.25133484],       [-3.6053038 , -0.04224385],       [ 1.38340578,  0.2935787 ],       [ 2.22189802, -0.25133484],       [ 3.6053038 ,  0.04224385]])      

So you want to fit RandomizedPCA and then transform as:

In [20]: pca = RandomizedPCA(n_components=3)In [21]: pca.fit(X)Out[21]: RandomizedPCA(copy=True, iterated_power=3, n_components=3, random_state=None,       whiten=False)In [22]: pca.transform(z)Out[22]: array([[ 2.76681156,  0.58715739],       [ 1.92831932,  1.13207093],       [ 0.54491354,  0.83849224],       [ 5.53362311,  1.17431479],       [ 6.37211535,  0.62940125],       [ 7.75552113,  0.92297994]])In [23]: 

In particular PCA .transform applies the change of basis obtained through the PCA decomposition of the matrix X to the matrix Z.