Chapter 8. Machine Learning Models with scikit-learn

In the previous chapter, we saw how to perform data munging, data aggregation, and grouping. In this chapter, we will see the working of different scikit-learn modules for different models in brief, data representation in scikit-learn, understand supervised and unsupervised learning using an example, and measure prediction performance.

An overview of machine learning models

Machine learning is a subfield of artificial intelligence that explores how machines can learn from data to analyze structures, help with decisions, and make predictions. In 1959, Arthur Samuel defined machine learning as the, "Field of study that gives computers the ability to learn without being explicitly programmed."

A wide range of applications employ machine learning methods, such as spam filtering, optical character recognition, computer vision, speech recognition, credit approval, search engines, and recommendation systems.

One important driver for machine learning is the fact that data is generated at an increasing pace across all sectors; be it web traffic, texts or images, and sensor data or scientific datasets. The larger amounts of data give rise to many new challenges in storage and processing systems. On the other hand, many learning algorithms will yield better results with more data to learn from. The field has received a lot of attention...

The scikit-learn modules for different models

The scikit-learn library is organized into submodules. Each submodule contains algorithms and helper methods for a certain class of machine learning models and approaches.

Here is a sample of those submodules, including some example models:

Data representation in scikit-learn

In contrast to the heterogeneous domains and applications of machine learning, the data representation in scikit-learn is less diverse, and the basic format that many algorithms expect is straightforward—a matrix of samples and features.

The underlying data structure is a numpy and the ndarray. Each row in the matrix corresponds to one sample and each column to the value of one feature.

There is something like Hello World in the world of machine learning datasets as well; for example, the Iris dataset whose origins date back to 1936. With the standard installation of scikit-learn, you already have access to a couple of datasets, including Iris that consists of 150 samples, each consisting of four measurements taken from three different Iris flower species:

>>> import numpy as np
>>> from sklearn import datasets
>>> iris = datasets.load_iris()

The dataset is packaged as a bunch, which is only a thin wrapper around a dictionary:

...

Supervised learning – classification and regression

In this section, we will show short examples for both classification and regression.

Classification problems are pervasive: document categorization, fraud detection, market segmentation in business intelligence, and protein function prediction in bioinformatics.

While it might be possible for hand-craft rules to assign a category or label to new data, it is faster to use algorithms to learn and generalize from the existing data.

We will continue with the Iris dataset. Before we apply a learning algorithm, we want to get an intuition of the data by looking at some values and plots.

All measurements share the same dimension, which helps to visualize the variance in various boxplots:

We see that the petal length (the third feature) exhibits the biggest variance, which could indicate the importance of this feature during classification. It is also insightful to plot the data points in two dimensions, using one feature for each axis. Also, indeed...

Unsupervised learning – clustering and dimensionality reduction

A lot of existing data is not labeled. It is still possible to learn from data without labels with unsupervised models. A typical task during exploratory data analysis is to find related items or clusters. We can imagine the Iris dataset, but without the labels:

While the task seems much harder without labels, one group of measurements (in the lower-left) seems to stand apart. The goal of clustering algorithms is to identify these groups.

We will use K-Means clustering on the Iris dataset (without the labels). This algorithm expects the number of clusters to be specified in advance, which can be a disadvantage. K-Means will try to partition the dataset into groups, by minimizing the within-cluster sum of squares.

For example, we instantiate the KMeans model with n_clusters equal to 3:

>>> from sklearn.cluster import KMeans
>>> km = KMeans(n_clusters=3)

Similar to supervised algorithms, we can use the fit methods...

Measuring prediction performance

We have already seen that the machine learning process consists of the following steps:

• Model selection: We first select a suitable model for our data. Do we have labels? How many samples are available? Is the data separable? How many dimensions do we have? As this step is nontrivial, the choice will depend on the actual problem. As of Fall 2015, the scikit-learn documentation contains a much appreciated flowchart called choosing the right estimator. It is short, but very informative and worth taking a closer look at.

• Training: We have to bring the model and data together, and this usually happens in the fit methods of the models in scikit-learn.

• Application: Once we have trained our model, we are able to make predictions about the unseen data.

So far, we omitted an important step that takes place between the training and application: the model testing and validation. In this step, we want to evaluate how well our model has learned.

One goal of learning, and...

Summary

In this chapter, we took a whirlwind tour through one of the most popular Python machine learning libraries: scikit-learn. We saw what kind of data this library expects. Real-world data will seldom be ready to be fed into an estimator right away. With powerful libraries, such as Numpy and, especially, Pandas, you already saw how data can be retrieved, combined, and brought into shape. Visualization libraries, such as matplotlib, help along the way to get an intuition of the datasets, problems, and solutions.

During this chapter, we looked at a canonical dataset, the Iris dataset. We also looked at it from various angles: as a problem in supervised and unsupervised learning and as an example for model verification.

In total, we have looked at four different algorithms: the Support Vector Machine, Linear Regression, K-Means clustering, and Principal Component Analysis. Each of these alone is worth exploring, and we barely scratched the surface, although we were able to implement all...

The rest of the chapter is locked

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Renews at €14.99/month. Cancel anytime

Start free trial

Previous Chapter

Chapter 1 of 15

Next Chapter

You have been reading a chapter from

Getting Started with Python Data Analysis

Published in: Nov 2015 Publisher: ISBN-13: 9781785285110

© 2015 Packt Publishing Limited All Rights Reserved

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Sign up now

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €14.99/month. Cancel anytime}

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

Aug 2023 7 hours 40 minutes

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

Dec 2023 12 hours 0 minutes

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

Aug 2023 22 hours 48 minutes

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

Sep 2023 8 hours 36 minutes

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

Sep 2023 8 hours 36 minutes

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

Oct 2023 21 hours 12 minutes

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

Aug 2023 14 hours 0 minutes

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

Dec 2023 8 hours 0 minutes

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

Nov 2023 22 hours 8 minutes

Machine Learning Kubernetes React Python Cybersecurity Unity Game Development Power BI Azure Web Development

Legal

Terms and Conditions Privacy Policy Cookie Policy Shipping Policy Cancellation Policy Return Policy

Support

Help Contact Us

Business

Partnerships

Sponsored eBooks Custom eBooks

Careers Become an author

Packt+ Membership

Subscription DataPro SecPro TechLeaders

Estonia

Company Address: Packt Publishing Ltd, Grosvenor House, 11 St Paul's Square, Birmingham, B3 1RB © 2024 Packt Publishing Limited All Rights Reserved

Submodule	Description	Example models
cluster	This is the unsupervised clustering	KMeans and Ward
decomposition	This is the dimensionality reduction	PCA and NMF
ensemble	This involves ensemble-based methods	AdaBoostClassifier, AdaBoostRegressor, RandomForestClassifier, RandomForestRegressor
lda	This stands for latent discriminant analysis	LDA
linear_model	This is the generalized linear model	LinearRegression, LogisticRegression, Lasso and Perceptron
mixture	This is the mixture model	GMM and VBGMM
naive_bayes	This involves supervised learning based on Bayes' theorem	BaseNB and BernoulliNB, GaussianNB
neighbors	These are k-nearest neighbors	KNeighborsClassifier, KNeighborsRegressor...

Data representation in scikit-learn

In contrast to the heterogeneous domains and applications of machine learning, the data representation in scikit-learn is less diverse, and the basic format that many algorithms expect is straightforward—a matrix of samples and features.

The underlying data structure is a numpy and the ndarray. Each row in the matrix corresponds to one sample and each column to the value of one feature.

There is something like Hello World in the world of machine learning datasets as well; for example, the Iris dataset whose origins date back to 1936. With the standard installation of scikit-learn, you already have access to a couple of datasets, including Iris that consists of 150 samples, each consisting of four measurements taken from three different Iris flower species:

>>> import numpy as np
>>> from sklearn import datasets
>>> iris = datasets.load_iris()

The dataset is packaged as a bunch, which is only a thin wrapper around a dictionary:

...

Supervised learning – classification and regression

In this section, we will show short examples for both classification and regression.

Classification problems are pervasive: document categorization, fraud detection, market segmentation in business intelligence, and protein function prediction in bioinformatics.

While it might be possible for hand-craft rules to assign a category or label to new data, it is faster to use algorithms to learn and generalize from the existing data.

We will continue with the Iris dataset. Before we apply a learning algorithm, we want to get an intuition of the data by looking at some values and plots.

All measurements share the same dimension, which helps to visualize the variance in various boxplots:

We see that the petal length (the third feature) exhibits the biggest variance, which could indicate the importance of this feature during classification. It is also insightful to plot the data points in two dimensions, using one feature for each axis. Also, indeed...

Unsupervised learning – clustering and dimensionality reduction

A lot of existing data is not labeled. It is still possible to learn from data without labels with unsupervised models. A typical task during exploratory data analysis is to find related items or clusters. We can imagine the Iris dataset, but without the labels:

While the task seems much harder without labels, one group of measurements (in the lower-left) seems to stand apart. The goal of clustering algorithms is to identify these groups.

We will use K-Means clustering on the Iris dataset (without the labels). This algorithm expects the number of clusters to be specified in advance, which can be a disadvantage. K-Means will try to partition the dataset into groups, by minimizing the within-cluster sum of squares.

For example, we instantiate the KMeans model with n_clusters equal to 3:

>>> from sklearn.cluster import KMeans
>>> km = KMeans(n_clusters=3)

Similar to supervised algorithms, we can use the fit methods...

Measuring prediction performance

We have already seen that the machine learning process consists of the following steps:

• Model selection: We first select a suitable model for our data. Do we have labels? How many samples are available? Is the data separable? How many dimensions do we have? As this step is nontrivial, the choice will depend on the actual problem. As of Fall 2015, the scikit-learn documentation contains a much appreciated flowchart called choosing the right estimator. It is short, but very informative and worth taking a closer look at.

• Training: We have to bring the model and data together, and this usually happens in the fit methods of the models in scikit-learn.

• Application: Once we have trained our model, we are able to make predictions about the unseen data.

So far, we omitted an important step that takes place between the training and application: the model testing and validation. In this step, we want to evaluate how well our model has learned.

One goal of learning, and...

Summary

In this chapter, we took a whirlwind tour through one of the most popular Python machine learning libraries: scikit-learn. We saw what kind of data this library expects. Real-world data will seldom be ready to be fed into an estimator right away. With powerful libraries, such as Numpy and, especially, Pandas, you already saw how data can be retrieved, combined, and brought into shape. Visualization libraries, such as matplotlib, help along the way to get an intuition of the datasets, problems, and solutions.

During this chapter, we looked at a canonical dataset, the Iris dataset. We also looked at it from various angles: as a problem in supervised and unsupervised learning and as an example for model verification.

In total, we have looked at four different algorithms: the Support Vector Machine, Linear Regression, K-Means clustering, and Principal Component Analysis. Each of these alone is worth exploring, and we barely scratched the surface, although we were able to implement all...