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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.
Key Features
- Build your first machine learning model using scikit-learn
- Train supervised and unsupervised models using popular techniques such as classification, regression and clustering
- Understand how scikit-learn can be applied to different types of machine learning problems
Book Description
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.
What you will learn
- Learn how to work with all scikit-learn's machine learning algorithms
- Install and set up scikit-learn to build your first machine learning model
- Employ Unsupervised Machine Learning Algorithms to cluster unlabelled data into groups
- Perform classification and regression machine learning
- Use an effective pipeline to build a machine learning project from scratch
Who this book is for
This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 156
Veröffentlichungsjahr: 2018
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Machine Learning with scikit-learn Quick Start Guide
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Commissioning Editor: Amey VarangaonkarAcquisition Editor: Aditi GourContent Development Editor:Smit CarvalhoTechnical Editor: Jinesh TopiwalaCopy Editor: Safis EditingProject Coordinator: Hardik BhindeProofreader: Safis EditingIndexer:Tejal Daruwale SoniGraphics:Jason MonteiroProduction Coordinator:Jyoti Chauhan
First published: October 2018
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ISBN 978-1-78934-370-0
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Contributors
About the author
Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built.
Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.
About the reviewers
Mehar Pratap Singh is one of the co-founders of ProCogia, and divides his time between their corporate headquarters in Vancouver, BC, and their Seattle office. Among his priorities is the marshaling of the diverse talents at ProCogia to expand the possibilities of data science and give their clients unbeatable competitive advantages.
Mehar was previously a data science consultant at T-Mobile, Microsoft, and several start-up ventures in Seattle. He holds an MBA from the University of Washington and an MS in electrical engineering from the University of Wisconsin.
Mehar's favorite party trick is to recite the entire chemical periodic table from memory. He is also an avid basketball player and loves following the NBA.
Joydeep Bhattacharjee is a principal engineer working for Nineleaps Technology Solutions. After graduating from the National Institute of Technology at Silchar, he started working in the software industry, where he stumbled upon Python. Through Python, he came across machine learning. Now he primarily develops intelligent systems that can parse and process data to solve challenging problems at work. He believes in sharing knowledge and loves mentoring in machine learning. He has published a book on FastText, a popular natural language processing tool, and loves speaking about machine learning at various national and international conferences.
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Table of Contents
Title Page
Copyright and Credits
Machine Learning with scikit-learn Quick Start Guide
Dedication
Packt Upsell
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in action
Conventions used
Get in touch
Reviews
Introducing Machine Learning with scikit-learn
A brief introduction to machine learning
Supervised learning
Unsupervised learning
What is scikit-learn?
Installing scikit-learn
The pip method
The Anaconda method
Additional packages
Pandas
Matplotlib
Tree
Pydotplus
Image
Algorithms that you will learn to implement using scikit-learn
Supervised learning algorithms
Unsupervised learning algorithms
Summary
Predicting Categories with K-Nearest Neighbors
Technical requirements
Preparing a dataset for machine learning with scikit-learn
Dropping features that are redundant
Reducing the size of the data
Encoding the categorical variables
Missing values
The k-NN algorithm
Implementing the k-NN algorithm using scikit-learn
Splitting the data into training and test sets
Implementation and evaluation of your model
Fine-tuning the parameters of the k-NN algorithm
Scaling for optimized performance
Summary
Predicting Categories with Logistic Regression
Technical requirements
Understanding logistic regression mathematically 
Implementing logistic regression using scikit-learn
Splitting the data into training and test sets
Fine-tuning the hyperparameters
Scaling the data
Interpreting the logistic regression model
Summary
Predicting Categories with Naive Bayes and SVMs
Technical requirements
The Naive Bayes algorithm 
Implementing the Naive Bayes algorithm in scikit-learn
Support vector machines
Implementing the linear support vector machine algorithm in scikit-learn
Hyperparameter optimization for the linear SVMs
Graphical hyperparameter optimization
Hyperparameter optimization using GridSearchCV
Scaling the data for performance improvement
Summary
Predicting Numeric Outcomes with Linear Regression
Technical requirements
The inner mechanics of the linear regression algorithm
Implementing linear regression in scikit-learn
Linear regression in two dimensions 
Using linear regression to predict mobile transaction amount
Scaling your data
Model optimization 
Ridge regression
Lasso regression
Summary
Classification and Regression with Trees
Technical requirements
Classification trees
The decision tree classifier
Picking the best feature
The Gini coefficient
Implementing the decision tree classifier in scikit-learn
Hyperparameter tuning for the decision tree
Visualizing the decision tree
The random forests classifier
Implementing the random forest classifier in scikit-learn
Hyperparameter tuning for random forest algorithms
The AdaBoost classifier
Implementing the AdaBoost classifier in scikit-learn
Hyperparameter tuning for the AdaBoost classifier
Regression trees
The decision tree regressor
Implementing the decision tree regressor in scikit-learn
Visualizing the decision tree regressor
The random forest regressor
Implementing the random forest regressor in scikit-learn
The gradient boosted tree
Implementing the gradient boosted tree in scikit-learn
Ensemble classifier
Implementing the voting classifier in scikit-learn
Summary
Clustering Data with Unsupervised Machine Learning
Technical requirements
The k-means algorithm
Assignment of centroids
When does the algorithm stop iterating?
Implementing the k-means algorithm in scikit-learn
Creating the base k-means model
The optimal number of clusters
Feature engineering for optimization
Scaling
Principal component analysis
Cluster visualization
t-SNE
Hierarchical clustering
Step 1 – Individual features as individual clusters
Step 2 – The merge
Step 3 – Iteration
Implementing hierarchical clustering
Going from unsupervised to supervised learning
Creating a labeled dataset 
Building the decision tree
Summary
Performance Evaluation Methods
Technical requirements
Why is performance evaluation critical?
Performance evaluation for classification algorithms
The confusion matrix
The normalized confusion matrix
Area under the curve
Cumulative gains curve
Lift curve
K-S statistic plot
Calibration plot
Learning curve
Cross-validated box plot
Performance evaluation for regression algorithms
Mean absolute error
Mean squared error
Root mean squared error
Performance evaluation for unsupervised algorithms
Elbow plot
Summary
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Preface
The fundamental aim of this book is help its readers quickly deploy, optimize, and evaluate every kind of machine learning algorithm that scikit-learn provides in an agile manner.
Readers will learn how to deploy supervised machine learning algorithms, such as logistic regression, k-nearest neighbors, linear regression, Support Vector Machines, Naive Bayes, and tree-based algorithms, in order to solve classification and regression machine learning problems.
Readers will also learn how to deploy unsupervised machine learning algorithms such as the k-means algorithm in order to cluster unlabeled data into groups.
Finally, readers will be provided with different techniques to visually interpret and evaluate the performance of the algorithms that they build.
Who this book is for
This book is for data scientists, software engineers, and people interested in machine learning with a background in Python who would like to understand, implement, and evaluate a wide range of machine learning algorithms using the scikit-learn framework.
What this book covers
Chapter 1, Introducing Machine Learning with scikit-learn, is a brief introduction to the different types of machine learning and its applications.
Chapter 2, Predicting Categories with K-Nearest Neighbors, covers working with and implementing the k-nearest neighbors algorithm to solve classification problems in scikit-learn.
Chapter 3, Predicting Categories with Logistic Regression, explains the workings and implementation of the logistic regression algorithm when solving classification problems in scikit-learn.
Chapter 4, Predicting Categories with Naive Bayes and SVMs, explains the workings and implementation of the Naive Bayes and the Linear Support Vector Machines algorithms when solving classification problems in scikit-learn.
Chapter 5, Predicting Numeric Outcomes with Linear Regression, explains the workings and implementation of the linear regression algorithm when solving regression problems in scikit-learn.
Chapter 6, Classification and Regression with Trees, explains the workings and implementation of tree-based algorithms such as decision trees, random forests, and the boosting and ensemble algorithms when solving classification and regression problems in scikit-learn.
Chapter 7, Clustering Data with Unsupervised Machine Learning, explains the workings and implementation of the k-means algorithm when solving unsupervised problems in scikit-learn.
Chapter 8, Performance Evaluation Methods, contains visual performance evaluation techniques for supervised and unsupervised machine learning algorithms.
To get the most out of this book
To get the most out of this book:
Prior knowledge of Python is assumed at a basic level.
Jupyter Notebook as a development environment is preferred but not necessary.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
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The code bundle for the book is also hosted on GitHub athttps://github.com/PacktPublishing/Machine-Learning-with-scikit-learn-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Code in action
Visit the following link to check out videos of the code being run:http://bit.ly/2OcWIGH
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Feedback from our readers is always welcome.
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Reviews
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Introducing Machine Learning with scikit-learn
Welcome to the world of machine learning with scikit-learn. I'm thrilled that you have chosen this book in order to begin or further advance your knowledge on the vast field of machine learning. Machine learning can be overwhelming at times and this is partly due to the large number of tools that are available on the market. This book will simplify this process of tool selection down to one – scikit-learn.
If I were to tell you what this book can do for you in one sentence, it would be this –The book gives you pipelines that can be implemented in order to solve a wide range of machine learning problems. True to what this sentence implies, you will learn how to construct an end-to-end machine learning pipeline using some of the most popular algorithms that are widely used in the industry and professional competitions, such as Kaggle.
However, in this introductory chapter, we will go through the following topics:
A brief introduction to machine learning
What is scikit-learn?
Installing scikit-learn
Algorithms that you will learn to implement scikit-learn in this book
Now, let's begin this fun journey into the world of machine learning with scikit-learn!
A brief introduction to machine learning
Machine learning has generated quite the buzz – from Elon Musk fearing the role of unregulated artificial intelligence in society, to Mark Zuckerberg having a view that contradicts Musk's.
So, what exactly is machine learning? Simply put, machine learning is a set ofmethodsthat can detect patterns in data and use those patterns to make future predictions. Machine learning has found immense value in a wide range of industries, ranging from finance to healthcare. This translates to a higher requirement of talent with the skill capital in the field of machine learning.
Broadly speaking, machine learning can be categorized into three main types:
Supervised learning
Unsupervised learning
Reinforcement learning
Scikit-learn is designed to tackle problems pertaining to supervised and unsupervised learning only, and does not support reinforcement learning at present.
Supervised learning
Supervised learning is a form of machine learning in which our data comes with a set of labels or a target variable that is numeric. These labels/categories usually belong to one feature/attribute, which is commonly known as the target variable. For instance, each row of your data could either belong to the category of Healthy or Not Healthy.
Given a set of features such as weight, blood sugar levels, and age, we can use the supervised machine learning algorithm to predict whether the person is healthy or not.
In the following simple mathematical expression, S is the supervised learning algorithm, X is the set of input features, such as weight and age, and Y is the target variable with the labels Healthy or Not Healthy:
Although supervised machine learning is the most common type of machine learning that is implemented with scikit-learn and in the industry, most datasets typically do not come with predefined labels. Unsupervised learning algorithms are first used to cluster data without labels into distinct groups to which we can then assign labels. This is discussed in detail in the following section.
Unsupervised learning
Unsupervised learning is a form of machine learning in which the algorithm tries to detect/find patterns in data that do not have an outcome/target variable. In other words, we do not have data that comes with pre-existing labels. Thus, the algorithm will typically use a metric such as distance to group data together depending on how close they are to each other.
As discussed in the previous section, most of the data that you will encounter in the real world will not come with a set of predefined labels and, as such, will only have a set of input features without a target attribute.
In the following simple mathematical expression, U is the unsupervised learning algorithm, while X is a set of input features, such as weight and age:
Given this data, our objective is to create groups that could potentially be labeled as Healthy or Not Healthy. The unsupervised learning algorithm will use a metric such as distance in order to identify how close a set of points are to each other and how far apart two such groups are. The algorithm will then proceed to cluster these groups into two distinct groups, as illustrated in the following diagram:
What is scikit-learn?
Scikit-learn is a free and open source software that helps you tackle supervised and unsupervised machine learning problems. The software is built entirely in Python and utilizes some of the most popular libraries that Python has to offer, namely NumPy and SciPy.
The main reason why scikit-learn is very popular stems from the fact that most of the world's most popular machine learning algorithms can be implemented quite quickly in a plug and play format once you know what the core pipeline is like. Another reason is that popular algorithms for classification such as logistic regression and support vector machines are written in Cython. Cython is used to give these algorithms C-like performance and thus makes the use of scikit-learn quite efficient in the process.
Installing scikit-learn
There are two ways in which you can install scikit-learn on your personal device:
By using the pip method
By using the Anaconda method
The pip method can be implemented on the macOS/Linux Terminal or the Windows PowerShell, while the Anaconda method will work with the Anaconda prompt.
Choosing between these two methods of installation is pretty straightforward:
If you would like all the common Python package distributions for data science to be installed in one environment, the Anaconda method works best
If you would like to build you own environment from scratch for scikit-learn, the pip method works best (for advanced users of Python)
The pip method
Scikit-learn requires a few packages to be installed on your device before you can install it. These are as follows:
NumPy
: Version 1.8.2 or greater
SciPy
: Version 0.13.3 or greater
These can be installed using the pip method by using the following commands:
pip3 install NumPy
pip3 install SciPy
Next, we can install scikit-learn using the following code:
pip3 install scikit-learn
Additionally, if you already have scikit-learn installed on your device and you simply want to upgrade it to the latest version, you can use the following code:
pip3 install -U scikit-learn
The Anaconda method
In the event that you have installed Python using the Anaconda distribution, you can install scikit-learn by using the following code in the Anaconda prompt:
The first step is to install the dependencies:
conda install NumPy
conda install SciPy
Next, we can install scikit-learn by using the following code:
conda install scikit-learn
Additionally, if you already have scikit-learn installed with the Anaconda distribution, you can upgrade it to the latest version by using the following code in the Anaconda prompt:
conda update scikit-learn
Additional packages
In this section, we will talk about the packages that we will be installing outside of scikit-learn that will be used throughout this book.
Pandas
To install Pandas, you can use either the pip method or the Anaconda method, as follows:
Pip method:
pip3 install pandas
Anaconda method:
conda install pandas
Matplotlib
To install matplotlib, you can use either the pip method or the Anaconda method, as follows:
Pip method:
pip3 install matplotlib
Anaconda method:
conda install matplotlib
Tree
To install tree, you can use either the pip method or the Anaconda method, as follows:
Pip method:
pip3 install tree
Anaconda method:
conda install tree
Pydotplus
To install pydotplus, you can use either the pip method or the Anaconda method, as follows:
Pip method:
pip3 install pydotplus
Anaconda method:
conda install pydotplus
Image
To install Image, you can use either the pip method or the Anaconda method, as follows:
Pip method:
pip3 install Image
Anaconda method:
conda install Image
Algorithms that you will learn to implement using scikit-learn
The algorithms that you will learn about in this book are broadly classified into the following two categories:
Supervised learning algorithms
Unsupervised learning algorithms
Supervised learning algorithms
Supervised learning algorithms can be used to solve both classification and regression problems. In this book, you will learn how to implement some of the most popular supervised machine learning algorithms. Popular supervised machine learning algorithms are the ones that are widely used in industry and research, and have helped us solve a wide range of problems across a wide range of domains. These supervised learning algorithms are as follows:
Linear
regression
: This supervised learning algorithm is used to predict continuous numeric outcomes such as house prices, stock prices, and temperature, to name a few
Logistic regression
: The logistic learning algorithm is a popular classification algorithm that is especially used in the credit industry in order to predict loan defaults
k-Nearest Neighbors
:
The k-NN algorithm is a classification algorithm that is used to classify data into two or more categories, and is widely used to classify houses into expensive and affordable categories based on price, area, bedrooms, and a whole range of other features
Support vector machines
:
