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

Building a ridge regressor

One of the main problems of linear regression is that it's sensitive to outliers. During data collection in the real world, it's quite common to wrongly measure the output. Linear regression uses ordinary least squares, which tries to minimize the squares of errors. The outliers tend to cause problems because they contribute a lot to the overall error. This tends to disrupt the entire model.

Getting ready

Let's consider the following figure:

The two points on the bottom are clearly outliers, but this model is trying to fit all the points. Hence, the overall model tends to be inaccurate. By visual inspection, we can see that the following figure is a better model:

Ordinary least squares considers every single datapoint when it's building the model. Hence, the actual model ends up looking like the dotted line as shown in the preceding figure. We can clearly see that this model is suboptimal. To avoid this, we use regularization where a penalty is imposed on the size of the coefficients. This method is called Ridge Regression.

How to do it…

Let's see how to build a ridge regressor in Python:

You can load the data from the data_multi_variable.txt file. This file contains multiple values in each line. All the values except the last value form the input feature vector.
Add the following lines to regressor.py. Let's initialize a ridge regressor with some parameters:
```
ridge_regressor = linear_model.Ridge(alpha=0.01, fit_intercept=True, max_iter=10000)
```
The alpha parameter controls the complexity. As alpha gets closer to 0, the ridge regressor tends to become more like a linear regressor with ordinary least squares. So, if you want to make it robust against outliers, you need to assign a higher value to alpha. We considered a value of 0.01, which is moderate.

Let's train this regressor, as follows:

ridge_regressor.fit(X_train, y_train)
y_test_pred_ridge = ridge_regressor.predict(X_test)
print "Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred_ridge), 2) 
print "Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred_ridge), 2) 
print "Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred_ridge), 2) 
print "Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred_ridge), 2) 
print "R2 score =", round(sm.r2_score(y_test, y_test_pred_ridge), 2)

Run this code to view the error metrics. You can build a linear regressor to compare and contrast the results on the same data to see the effect of introducing regularization into the model.

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

Building a ridge regressor

Getting ready

How to do it…