Book Image

Hands-On Automated Machine Learning

By : Sibanjan Das, Umit Mert Cakmak
Book Image

Hands-On Automated Machine Learning

By: Sibanjan Das, Umit Mert Cakmak

Overview of this book

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.
Table of Contents (10 chapters)

A complex pipeline

In this section, we will determine the best classifier to predict the species of an Iris flower using its four different features. We will use a combination of four different data preprocessing techniques along with four different ML algorithms for the task. The following is the pipeline design for the job:

We will proceed as follows:

  1. We start with importing the various libraries and functions that are required for the task:
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn import tree
from sklearn.pipeline import Pipeline
  1. Next, we load the Iris dataset...