Difference between ‘transform’ and ‘fit_transform’ in scikit-learn

When working with machine learning models in scikit-learn, you may come across two similar methods – ‘transform’ and ‘fit_transform’. While both of these methods are used for feature transformation in scikit-learn, there is a key difference between them.

Transform:

The ‘transform’ method is used to apply a transformation to a dataset without altering the actual transformation itself. This means that the ‘transform’ method is used after fitting a transformation on a training dataset, and then applying the same transformation to a testing dataset. The ‘transform’ method is useful for applying the same transformation to multiple datasets without having to refit the transformation each time.

Fit_transform:

The ‘fit_transform’ method, on the other hand, performs two steps in one – it fits a transformation on the training dataset and then applies the transformation to the same dataset. This means that the ‘fit_transform’ method is used to both learn the parameters of the transformation and apply it to the dataset in a single step.

Essentially, the ‘fit_transform’ method is a shortcut for calling ‘fit’ and then ‘transform’ separately. It is commonly used when working with scikit-learn pipeline objects, where the entire data preprocessing and model fitting workflow is encapsulated in a single object.

Conclusion:

In summary, the main difference between ‘transform’ and ‘fit_transform’ in scikit-learn is that ‘transform’ applies a pre-fitted transformation to a dataset, while ‘fit_transform’ learns the transformation parameters on the dataset itself and applies the transformation in one step. Depending on your workflow, you may choose to use one method over the other to efficiently preprocess your data and build accurate machine learning models.