Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.

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Table of Contents (10 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing Machine Learning with scikit-learn
Introducing Machine Learning with scikit-learn
A brief introduction to machine learning
What is scikit-learn?
Installing scikit-learn
Algorithms that you will learn to implement using scikit-learn
Summary
2
Predicting Categories with K-Nearest Neighbors
Predicting Categories with K-Nearest Neighbors
Technical requirements
Preparing a dataset for machine learning with scikit-learn
The k-NN algorithm
Implementing the k-NN algorithm using scikit-learn
Fine-tuning the parameters of the k-NN algorithm
Scaling for optimized performance
Summary
3
Predicting Categories with Logistic Regression
Predicting Categories with Logistic Regression
Technical requirements
Understanding logistic regression mathematically
Implementing logistic regression using scikit-learn
Fine-tuning the hyperparameters
Scaling the data
Interpreting the logistic regression model
Summary
4
Predicting Categories with Naive Bayes and SVMs
Predicting Categories with Naive Bayes and SVMs
Technical requirements
The Naive Bayes algorithm
Support vector machines
Summary
5
Predicting Numeric Outcomes with Linear Regression
Predicting Numeric Outcomes with Linear Regression
Technical requirements
The inner mechanics of the linear regression algorithm
Implementing linear regression in scikit-learn
Model optimization
Summary
6
Classification and Regression with Trees
Classification and Regression with Trees
Technical requirements
Classification trees
Regression trees
Ensemble classifier
Summary
7
Clustering Data with Unsupervised Machine Learning
Clustering Data with Unsupervised Machine Learning
Technical requirements
The k-means algorithm
Implementing the k-means algorithm in scikit-learn
Feature engineering for optimization
Cluster visualization
Going from unsupervised to supervised learning
Summary
8
Performance Evaluation Methods
Performance Evaluation Methods
Technical requirements
Why is performance evaluation critical?
Performance evaluation for classification algorithms
Performance evaluation for regression algorithms
Performance evaluation for unsupervised algorithms
Summary
9
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Fine-tuning the parameters of the k-NN algorithm

In the previous section, we arbitrarily set the number of neighbors to three while initializing the k-NN classifier. However, is this the optimal value? Well, it could be, since we obtained a relatively high accuracy score in the test set.

Our goal is to create a machine learning model that does not overfit or underfit the data. Overfitting the data means that the model has been trained very specifically to the training examples provided and will not generalize well to cases/examples of data that it has not encountered before. For instance, we might have fit the model very specifically to the training data, with the test cases being also very similar to the training data. Thus, the model would have been able to perform very well and produce a very high value of accuracy.

Underfitting is another extreme case, in which the model...

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