Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Building machine learning pipelines

The scikit-learn library has provisions to build machine learning pipelines. We just need to specify the functions, and it will build a composed object that makes the data go through the whole pipeline. This pipeline can include functions, such as preprocessing, feature selection, supervised learning, unsupervised learning, and so on. In this recipe, we will be building a pipeline to take the input feature vector, select the top k features, and then classify them using a random forest classifier.

How to do it…

  1. Create a new Python file, and import the following packages:

    from sklearn.datasets import samples_generator
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.feature_selection import SelectKBest, f_regression
    from sklearn.pipeline import Pipeline
  2. Let's generate some sample data to play with:

    # generate sample data
    X, y = samples_generator.make_classification(
            n_informative=4, n_features=20, n_redundant=0, random_state=5)

    This line...