Book Image

scikit-learn Cookbook - Second Edition

By : Trent Hauck
Book Image

scikit-learn Cookbook - Second Edition

By: Trent Hauck

Overview of this book

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
Table of Contents (13 chapters)

Introduction

In this chapter, we focus on decision trees and ensemble algorithms. Decision algorithms are easy to interpret and visualize as they are outlines of the decision making process we are familiar with. Ensembles can be partially interpreted and visualized, but they have many parts (base estimators), so we cannot always read them easily.

The goal of ensemble learning is that several estimators can work better than a single one. There are two families of ensemble methods implemented in scikit-learn: averaging methods and boosting methods. Averaging methods (random forest, bagging, extra trees) reduce variance by averaging the predictions of several estimators. Boosting methods (gradient boost and AdaBoost) reduce bias by sequential building base estimators with the goal of reducing the bias of the whole ensemble.

A common characteristic of many ensemble constructions is...