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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Implement scikit-learn into every step of the data science pipeline
About This Book
- Use Python and scikit-learn to create intelligent applications
- Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain
- A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn
Who This Book Is For
If you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the course for you. No previous experience with machine-learning algorithms is required.
What You Will Learn
- Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics
- Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naive Bayes
- Use Decision Trees to explain the main causes of certain phenomena such as passenger survival on the Titanic
- Evaluate the performance of machine learning systems in common tasks
- Master algorithms of various levels of complexity and learn how to analyze data at the same time
- Learn just enough math to think about the connections between various algorithms
- Customize machine learning algorithms to fit your problem, and learn how to modify them when the situation calls for it
- Incorporate other packages from the Python ecosystem to munge and visualize your dataset
- Improve the way you build your models using parallelization techniques
In Detail
Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning.
Style and Approach
Implement scikit-learn using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of scikit-learn.
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Seitenzahl: 567
Veröffentlichungsjahr: 2017
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Table of Contents
scikit-learn: Machine Learning Simplified
scikit-learn: Machine Learning Simplified
Copyright © 2017 Packt Publishing
All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Published on: November 2017
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-84719-752-8
www.packtpub.com
Credits
Authors
Raúl Garreta
Guillermo Moncecchi
Trent Hauck
Gavin Hackeling
Reviewers
Andreas Hjortgaard Danielsen
Noel Dawe
Gavin Hackeling
Anoop Thomas Mathew
Xingzhong
Fahad Arshad
Sarah Guido
Mikhail Korobov
Aman Madaan
Content Development Editor
Mayur Pawanikar
Production Coordinator
Arvindkumar Gupta
Preface
Suppose you want to predict whether tomorrow will be a sunny or rainy day. You can develop an algorithm that is based on the current weather and your meteorological knowledge using a rather complicated set of rules to return the desired prediction. Now suppose that you have a record of the day-by-day weather conditions for the last five years, and you find that every time you had two sunny days in a row, the following day also happened to be a sunny one. Your algorithm could generalize this and predict that tomorrow will be a sunny day since the sun reigned today and yesterday. This algorithm is a pretty simple example of learning from experience. This is what Machine Learning is all about: algorithms that learn from the available data.
This course is designed in the same way that many data science and analytics projects play out. First, we need to acquire data; the data is often messy, incomplete, or not correct in some way. Therefore, we spend the first chapter talking about strategies for dealing with bad data and ways to deal with other problems that arise from data. For example, what happens if we have too many features? How do we handle that?
What this learning path covers
Module 1, Learning scikit-learn: Machine Learning in Python, in this module, you will learn several methods for building Machine Learning applications that solve different real-world tasks, from document classification to image recognition. We will use Python, a simple, popular, and widely used programming language, and scikit-learn, an open source Machine Learning library. In each chapter of this module, we will present a different Machine Learning setting and a couple of well-studied methods as well as show step-by-step examples that use Python and scikit-learn to solve concrete tasks. We will also show you tips and tricks to improve algorithm performance, both from the accuracy and computational cost point of views.
Module 2, scikit-learn Cookbook, the first chapter of this module is your guide. The meat of this module will walk you through various algorithms and how to implement them into your workflow. And finally, we'll end with the postmodel workflow. This chapter is fairly agnostic to the other chapters of the module and can be applied to the various algorithms you'll learn up until the final chapter.
Module 3, Mastering Machine Learning with scikit-learn, in this module, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled excellent implementations of many machine learning models and algorithms under a simple yet versatile API.
This module is motivated by two goals:
What you need for this learning path
Module 1:
For running the module's examples, you will need a running Python environment, including the scikit-learn library and NumPy and SciPy mathematical libraries. The source code will be available in the form of IPython notebooks. For Chapter 4, Advanced Features, we will also include the Pandas Python library. Chapter 1, Machine Learning – A Gentle Introduction, shows how to install them in your operating system.
Module 2:
Here are the contents that will get the environment set up. This will allow you to follow along with the code in this module. This method may be easier for less-experienced Python developers:
dateutil==2.1
ipython==2.2.0
ipython-notebook==2.1.0
jinja2==2.7.3
markupsafe==0.18
matplotlib==1.3.1
numpy==1.8.1
patsy==0.3.0
pandas==0.14.1
pip==1.5.6
pydot==1.0.28
pyparsing==1.5.6
pytz==2014.4
pyzmq==14.3.1
scikit-learn==0.15.0
scipy==0.14.0
setuptools==3.6
six==1.7.3
ssl_match_hostname==3.4.0.2
tornado==3.2.2
Module 3:
The examples in this module assume that you have an installation of Python 2.7. The first chapter will describe methods to install scikit-learn 0.15.2, its dependencies, and other libraries on Linux, OS X, and Windows.
Who this learning path is for
If you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the book for you. No previous experience with machine-learning algorithms is required.
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Errata
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Part 1. Module 1
Learning scikit-learn: Machine Learning in Python
Experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library
Machine learning categories
Classification is only one of the possible machine learning problems that can be addressed with scikit-learn. We can organize them in the following categories:
Important concepts related to machine learning
The linear classifier we presented in the previous section could look too simple. What if we use a higher degree polynomial? What if we also take as features not only the sepal length and width, but also the petal length and the petal width? This is perfectly possible, and depending on the sample distribution, it could lead to a better fit to the training data, resulting in higher accuracy. The problem with this approach is that now we must estimate not only the three original parameters (the coefficients for x1, x2, and the interception point), but also the parameters for the new features x3 and x4 (petal length and width) and also the product combinations of the four features.
Intuitively, we would need more training data to adequately estimate these parameters. The number of parameters (and consequently, the amount of training data needed to adequately estimate them) would rapidly grow if we add more features or higher order terms. This phenomenon, present in every machine learning method, is called the idem curse of dimensionality: when the number of parameters of a model grows, the data needed to learn them grows exponentially.
This notion is closely related to the problem of overfitting mentioned earlier. As our training data is not enough, we risk producing a model that could be very good at predicting the target class on the training dataset but fail miserably when faced with new data, that is, our model does not have the generalization power. That is why it is so important to evaluate our methods on previously unseen data.
The general rule is that, in order to avoid overfitting, we should prefer simple (that is, with less parameters) methods, something that could be seen as an instantiation of the philosophical principle of Occam's razor, which states that among competing hypotheses, the hypothesis with the fewest assumptions should be selected.
However, we should also take into account Einstein's words:
"Everything should be made as simple as possible, but not simpler."
The idem curse of dimensionality may suggest that we keep our models simple, but on the other hand, if our model is too simple we run the risk of suffering from underfitting. Underfitting problems arise when our model has such a low representation power that it cannot model the data even if we had all the training data we want. We clearly have underfitting when our algorithm cannot achieve good performance measures even when measuring on the training set.
As a result, we will have to achieve a balance between overfitting and underfitting. This is one of the most important problems that we will have to address when designing our machine learning models.
Other key concepts to take into account are the idem bias and variance of a machine learning method. Consider an extreme method that, in a binary classification setting, always predicts the positive class for any new instance. Its predictions are, trivially, always the same, or in statistical terms, it has null variance; but it will fail to predict negative examples: it is very biased towards positive results. On the other hand, consider a method that predicts, for a new instance, the class of the nearest instance in the training set (in fact, this method exists, and it is called the 1-nearest neighbor). The generalization assumptions that this method uses are very small: it has a very low bias; but, if we change the training data, results could dramatically change, that is, its variance is very high. These are extreme examples of the bias-variance tradeoff. It can be shown that, no matter which method we are using, if we reduce bias, variance will increase, and vice versa.
Linear classifiers have generally low-variance: no matter what subset we select for training, results will be similar. However, if the data distribution (as in the case of the versicolor and virginica species) makes target classes not separable by a hyperplane, these results will be consistently wrong, that is, the method is highly biased.
On the other hand, kNN (a memory-based method we will not address in this book) has very low bias but high variance: the results are generally very good at describing training data but tend to vary greatly when trained on different training instances.
There are other important concepts related to real-world applications where our data will not come naturally as a list of real-valued features. In these cases, we will need to have methods to transform non real-valued features to real-valued ones. Besides, there are other steps related to feature standardization and normalization, which as we saw in our Iris example, are needed to avoid undesired effects regarding the different value ranges. These transformations on the feature space are known as data preprocessing.
After having a defined feature set, we will see that not all of the features that come in our original dataset could be useful for resolving our task. So we must also have methods to do feature selection, that is, methods to select the most promising features.
In this book, we will present several problems and in each of them we will show different ways to transform and find the most relevant features to use for learning a task, called feature engineering, which is based on our knowledge of the domain of the problem and/or data analysis methods. These methods, often not valued enough, are a fundamental step toward obtaining good results.
Summary
In this chapter, we introduced the main general concepts in machine learning and presented scikit-learn, the Python library we will use in the rest of this book. We included a very simple example of classification, trying to show the main steps for learning, and including the most important evaluation measures we will use. In the rest of this book, we plan to show you different machine learning methods and techniques using different real-world examples for each one. In almost every computational task, the presence of historical data could allow us to improve performance in the sense introduced at the beginning of this chapter.
The next chapter introduces supervised learning methods: we have annotated data (that is, instances where the target class/value is known) and we want to predict the same class/value for future data from the same population. In the case of classification tasks, that is, a discrete-valued target class, several different models exist, ranging from statistical methods, such as the simple Naïve Bayes to advanced linear classifiers, such as Support Vector Machines (SVM). Some methods, such as decision trees, will allow us to visualize how important a feature is to discriminate between different target classes and have a human interpretation of the decision process. We will also address another type of supervised learning task: regression, that is, methods that try to predict real-valued data.
