Transforming the Prediction Target

Introduction

In machine learning, it is often necessary to transform the prediction target before training a model. This can include tasks such as converting multiclass labels into a binary indicator matrix or encoding non-numerical labels into numerical labels.

In this lab, we will explore the various techniques provided by the sklearn.preprocessing module in scikit-learn to transform the prediction target.

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Skills Graph

Label Binarization

Label binarization is the process of converting multiclass labels into a binary indicator matrix. It can be achieved using the LabelBinarizer class.

from sklearn import preprocessing

## Create a LabelBinarizer instance
lb = preprocessing.LabelBinarizer()

## Fit the LabelBinarizer on a list of multiclass labels
lb.fit([1, 2, 6, 4, 2])

## Get the classes learned by the LabelBinarizer
lb.classes_

## Transform a list of multiclass labels into a binary indicator matrix
lb.transform([1, 6])

MultiLabel Binarization

MultiLabel binarization is the process of converting a collection of collections of labels into an indicator format. This can be achieved using the MultiLabelBinarizer class.

from sklearn.preprocessing import MultiLabelBinarizer

## Define a list of collections of labels
y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]

## Create a MultiLabelBinarizer instance and fit_transform the list of collections
MultiLabelBinarizer().fit_transform(y)

Label Encoding

Label encoding is the process of converting non-numerical labels into numerical labels. This can be achieved using the LabelEncoder class.

from sklearn import preprocessing

## Create a LabelEncoder instance
le = preprocessing.LabelEncoder()

## Fit the LabelEncoder on a list of non-numerical labels
le.fit(["paris", "paris", "tokyo", "amsterdam"])

## Get the classes learned by the LabelEncoder
list(le.classes_)

## Transform a list of non-numerical labels into numerical labels
le.transform(["tokyo", "tokyo", "paris"])

## Inverse transform numerical labels back to non-numerical labels
list(le.inverse_transform([2, 2, 1]))

Summary

In this lab, we learned how to transform the prediction target using various techniques provided by the sklearn.preprocessing module in scikit-learn. These techniques included label binarization, multi-label binarization, and label encoding.