Python Machine Learning Cookbook

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili

Buy this Book

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Buy this Book

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.

Python Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Preface

Free Chapter

The Realm of Supervised Learning

Introduction

Preprocessing data using different techniques

Label encoding

Building a linear regressor

Computing regression accuracy

Achieving model persistence

Building a ridge regressor

Building a polynomial regressor

Estimating housing prices

Computing the relative importance of features

Estimating bicycle demand distribution

Constructing a Classifier

Introduction

Building a simple classifier

Building a logistic regression classifier

Building a Naive Bayes classifier

Splitting the dataset for training and testing

Evaluating the accuracy using cross-validation

Visualizing the confusion matrix

Extracting the performance report

Evaluating cars based on their characteristics

Extracting validation curves

Extracting learning curves

Estimating the income bracket

Predictive Modeling

Introduction

Building a linear classifier using Support Vector Machine (SVMs)

Building a nonlinear classifier using SVMs

Tackling class imbalance

Extracting confidence measurements

Finding optimal hyperparameters

Building an event predictor

Estimating traffic

Clustering with Unsupervised Learning

Introduction

Clustering data using the k-means algorithm

Compressing an image using vector quantization

Building a Mean Shift clustering model

Grouping data using agglomerative clustering

Evaluating the performance of clustering algorithms

Automatically estimating the number of clusters using DBSCAN algorithm

Finding patterns in stock market data

Building a customer segmentation model

Building Recommendation Engines

Introduction

Building function compositions for data processing

Building machine learning pipelines

Finding the nearest neighbors

Constructing a k-nearest neighbors classifier

Constructing a k-nearest neighbors regressor

Computing the Euclidean distance score

Computing the Pearson correlation score

Finding similar users in the dataset

Generating movie recommendations

Analyzing Text Data

Introduction

Preprocessing data using tokenization

Stemming text data

Converting text to its base form using lemmatization

Dividing text using chunking

Building a bag-of-words model

Building a text classifier

Identifying the gender

Analyzing the sentiment of a sentence

Identifying patterns in text using topic modeling

Speech Recognition

Introduction

Reading and plotting audio data

Transforming audio signals into the frequency domain

Generating audio signals with custom parameters

Synthesizing music

Extracting frequency domain features

Building Hidden Markov Models

Building a speech recognizer

Dissecting Time Series and Sequential Data

Introduction

Transforming data into the time series format

Slicing time series data

Operating on time series data

Extracting statistics from time series data

Building Hidden Markov Models for sequential data

Building Conditional Random Fields for sequential text data

Analyzing stock market data using Hidden Markov Models

Image Content Analysis

Introduction

Operating on images using OpenCV-Python

Detecting edges

Histogram equalization

Detecting corners

Detecting SIFT feature points

Building a Star feature detector

Creating features using visual codebook and vector quantization

Training an image classifier using Extremely Random Forests

Building an object recognizer

Biometric Face Recognition

Introduction

Capturing and processing video from a webcam

Building a face detector using Haar cascades

Building eye and nose detectors

Performing Principal Components Analysis

Performing Kernel Principal Components Analysis

Performing blind source separation

Building a face recognizer using Local Binary Patterns Histogram

Deep Neural Networks

Introduction

Building a perceptron

Building a single layer neural network

Building a deep neural network

Creating a vector quantizer

Building a recurrent neural network for sequential data analysis

Visualizing the characters in an optical character recognition database

Building an optical character recognizer using neural networks

Visualizing Data

Introduction

Plotting 3D scatter plots

Plotting bubble plots

Animating bubble plots

Drawing pie charts

Plotting date-formatted time series data

Plotting histograms

Visualizing heat maps

Animating dynamic signals

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Computing regression accuracy

Now that we know how to build a regressor, it's important to understand how to evaluate the quality of a regressor as well. In this context, an error is defined as the difference between the actual value and the value that is predicted by the regressor.

Getting ready

Let's quickly understand what metrics can be used to measure the quality of a regressor. A regressor can be evaluated using many different metrics, such as the following:

Mean absolute error: This is the average of absolute errors of all the datapoints in the given dataset.
Mean squared error: This is the average of the squares of the errors of all the datapoints in the given dataset. It is one of the most popular metrics out there!
Median absolute error: This is the median of all the errors in the given dataset. The main advantage of this metric is that it's robust to outliers. A single bad point in the test dataset wouldn't skew the entire error metric, as opposed to a mean error metric.
Explained variance score: This score measures how well our model can account for the variation in our dataset. A score of 1.0 indicates that our model is perfect.
R2 score: This is pronounced as R-squared, and this score refers to the coefficient of determination. This tells us how well the unknown samples will be predicted by our model. The best possible score is 1.0, and the values can be negative as well.

How to do it…

There is a module in scikit-learn that provides functionalities to compute all the following metrics. Open a new Python file and add the following lines:

import sklearn.metrics as sm

print "Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2) 
print "Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2) 
print "Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2) 
print "Explained variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2) 
print "R2 score =", round(sm.r2_score(y_test, y_test_pred), 2)

Keeping track of every single metric can get tedious, so we pick one or two metrics to evaluate our model. A good practice is to make sure that the mean squared error is low and the explained variance score is high.

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

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Current Title:

Python Machine Learning Cookbook

Computing regression accuracy

Getting ready

How to do it…