Our imaginations have long been captivated by visions of machines that can learn and imitate human intelligence. While machines capable of general artificial intelligence-like Arthur C. Clarke's HAL and Isaac Asimov's Sonny-have yet to be realized, software programs that can acquire new knowledge and skills through experience are becoming increasingly common. We use such machine learning programs to discover new music that we might enjoy, and to find exactly the shoes we want to purchase online. Machine learning programs allow us to dictate commands to our smart phones, and allow our thermostats to set their own temperatures. Machine learning programs can decipher sloppily-written mailing addresses better than humans, and can guard credit cards from fraud more vigilantly. From investigating new medicines to estimating the page views for versions of a headline, machine learning software is becoming central to many industries. Machine learning has even encroached on activities that have long been considered uniquely human, such as writing the sports column recapping the Duke basketball team's loss to UNC.

Machine learning is the design and study of software artifacts that use past experience to inform future decisions; machine learning is the study of programs that learn from data. The fundamental goal of machine learning is to generalize, or to induce an unknown rule from examples of the rule's application. The canonical example of machine learning is spam filtering. By observing thousands of emails that have been previously labeled as either spam or ham, spam filters learn to classify new messages. Arthur Samuel, a computer scientist who pioneered the study of artificial intelligence, said that machine learning is the "study that gives computers the ability to learn without being explicitly programmed". Throughout the 1950s and 1960s, Samuel developed programs that played checkers. While the rules of checkers are simple, complex strategies are required to defeat skilled opponents. Samuel never explicitly programmed these strategies, but through the experience of playing thousands of games, the program learned complex behaviors that allowed it to beat many human opponents.

A popular quote from computer scientist Tom Mitchell defines machine learning more formally: "A program can be said to learn from experience 'E' with respect to some class of tasks 'T' and performance measure 'P', if its performance at tasks in 'T', as measured by 'P', improves with experience 'E'." For example, assume that you have a collection of pictures. Each picture depicts either a dog or a cat. A task could be sorting the pictures into separate collections of dog and cat photos. A program could learn to perform this task by observing pictures that have already been sorted, and it could evaluate its performance by calculating the percentage of correctly classified pictures.

We will use Mitchell's definition of machine learning to organize this chapter. First, we will discuss types of experience, including supervised learning and unsupervised learning. Next, we will discuss common tasks that can be performed by machine learning systems. Finally, we will discuss performance measures that can be used to assess machine learning systems.

Machine learning systems are often described as learning from experience either with or without supervision from humans. In supervised learning problems, a program predicts an output for an input by learning from pairs of labeled inputs and outputs. That is, the program learns from examples of the "right answers". In unsupervised learning, a program does not learn from labeled data. Instead, it attempts to discover patterns in data. For example, assume that you have collected data describing the heights and weights of people. An example of an unsupervised learning problem is dividing the data points into groups. A program might produce groups that correspond to men and women, or children and adults. Now assume that the data is also labeled with the person's sex. An example of a supervised learning problem is to induce a rule for predicting whether a person is male or female based on his or her height and weight. We will discuss algorithms and examples of supervised and unsupervised learning in the following chapters.

Supervised learning and unsupervised learning can be thought of as occupying opposite ends of a spectrum. Some types of problem, called semi-supervised learning problems, make use of both supervised and unsupervised data; these problems are located on the spectrum between supervised and unsupervised learning. Reinforcement learning is located near the supervised end of the spectrum. Unlike supervised learning, reinforcement learning programs do not learn from labeled pairs of inputs and outputs. Instead, they receive feedback for their decisions, but errors are not explicitly corrected. For example, a reinforcement learning program that is learning to play a side-scrolling video game like Super Mario Bros may receive a reward when it completes a level or exceeds a certain score, and a punishment when it loses a life. However, this supervised feedback is not associated with specific decisions to run, avoid Goombas, or pick up fire flowers. We will focus primarily on supervised and unsupervised learning, as these categories include most common machine learning problems. In the next sections, we will review supervised and unsupervised learning in more detail.

A supervised learning program learns from labeled examples of the outputs that should be produced for an input. There are many names for the output of a machine learning program. Several disciplines converge in machine learning, and many of those disciplines use their own terminology. In this book, we will refer to the output as the response variable. Other names for response variables include "dependent variables", "regressands", "criterion variables", "measured variables", "responding variables", "explained variables", "outcome variables", "experimental variables", "labels", and "output variables". Similarly, the input variables have several names. In this book, we will refer to inputs as features, and the phenomena they represent as explanatory variables. Other names for explanatory variables include "predictors", "regressors", "controlled variables", and "exposure variables". Response variables and explanatory variables may take real or discrete values.

The collection of examples that comprise supervised experience is called a training set. A collection of examples that is used to assess the performance of a program is called a test set. The response variable can be thought of as the answer to the question posed by the explanatory variables; supervised learning problems learn from a collection of answers to different questions. That is, supervised learning programs are provided with the correct answers and must learn to respond correctly to unseen, but similar, questions.

Two of the most common supervised machine learning tasks are classification and regression. In classification tasks, the program must learn to predict discrete values for one or more response variables from one or more features. That is, the program must predict the most probable category, class, or label for new observations. Applications of classification include predicting whether a stock's price will rise or fall, or deciding whether a news article belongs to the politics or leisure sections. In regression problems, the program must predict the values of one more or continuous response variables from one or more features. Examples of regression problems include predicting the sales revenue for a new product, or predicting the salary for a job based on its description. Like classification, regression problems require supervised learning.

A common unsupervised learning task is to discover groups of related observations, called clusters, within the dataset. This task, called clustering or cluster analysis, assigns observations into groups such that observations within a groups are more similar to each other based on some similarity measure than they are to observations in other groups. Clustering is often used to explore a dataset. For example, given a collection of movie reviews, a clustering algorithm might discover the sets of positive and negative reviews. The system will not be able to label the clusters as positive or negative; without supervision, it will only have knowledge that the grouped observations are similar to each other by some measure. A common application of clustering is discovering segments of customers within a market for a product. By understanding what attributes are common to particular groups of customers, marketers can decide what aspects of their campaigns to emphasize. Clustering is also used by internet radio services; given a collection of songs, a clustering algorithm might be able to group the songs according to their genres. Using different similarity measures, the same clustering algorithm might group the songs by their keys, or by the instruments they contain.

Dimensionality reduction is another task that is commonly accomplished using unsupervised learning. Some problems may contain thousands or millions of features, which can be computationally costly to work with. Additionally, the program's ability to generalize may be reduced if some of the features capture noise or are irrelevant to the underlying relationship. Dimensionality reduction is the process of discovering the features that account for the greatest changes in the response variable. Dimensionality reduction can also be used to visualize data. It is easy to visualize a regression problem such as predicting the price of a home from its size; the size of the home can be plotted on the graph's x axis, and the price of the home can be plotted on the y axis. It is similarly easy to visualize the housing price regression problem when a second feature is added; the number of bathrooms in the house could be plotted on the z axis, for instance. A problem with thousands of features, however, becomes impossible to visualize.

As mentioned previously, a training set is a collection of observations. These observations comprise the experience that the algorithm uses to learn. In supervised learning problems, each observation consists of an observed response variable and features of one or more observed explanatory variables. The test set is a similar collection of observations. The test set is used to evaluate the performance of the model using some performance metric. It is important that no observations from the training set are included in the test set. If the test set does contain examples from the training set, it will be difficult to assess whether the algorithm has learned to generalize from the training set or has simply memorized it. A program that generalizes well will be able to effectively perform a task with new data. In contrast, a program that memorizes the training data by learning an overly-complex model could predict the values of the response variable for the training set accurately, but will fail to predict the value of the response variable for new examples. Memorizing the training set is called overfitting. A program that memorizes its observations may not perform its task well, as it could memorize relations and structure that are coincidental in the training data. Balancing generalization and memorization is a problem common to many machine learning algorithms. In later chapters we will discuss regularization, which can be applied to many models to reduce over-fitting.

In addition to the training and test data, a third set of observations, called a validation or hold-out set, is sometimes required. The validation set is used to tune variables called hyperparameters that control how the algorithm learns from the training data. The program is still evaluated on the test set to provide an estimate of its performance in the real world. The validation set should not be used to estimate real-world performance because the program has been tuned to learn from the training data in a way that optimizes its score on the validation data; the program will not have this advantage in the real world.

It is common to partition a single set of supervised observations into training, validation, and test sets. There are no requirements for the sizes of the partitions, and they may vary according to the amount of data available. It is common to allocate between fifty and seventy-five percent of the data to the training set, ten to twenty-five percent of the data to the test set, and the remainder to the validation set.

Some training sets may contain only a few hundred observations; others may include millions. Inexpensive storage, increased network connectivity, and the ubiquity of sensor-packed smartphones have contributed to the contemporary state of big data, or training sets with millions or billions of examples. While this book will not work with datasets that require parallel processing on tens or hundreds of computers, the predictive power of many machine learning algorithms improves as the amount of training data increases. However, machine learning algorithms also follow the maxim "garbage in, garbage out". A student who studies for a test by reading a large, confusing textbook that contains many errors likely will not score better than a student who reads a short but well-written textbook. Similarly, an algorithm trained on a large collection of noisy, irrelevant, or incorrectly-labeled data will not perform better than an algorithm trained on a smaller set of data that is more representative of the problem in the real-world.

Many supervised training sets are prepared manually or by semi-automated processes. Creating a large collection of supervised data can be costly in some domains. Fortunately, several datasets are bundled with scikit-learn, allowing developers to focus on experimenting with models instead. During development, and particularly when training data is scarce, a practice called cross-validation can be used to train and validate a model on the same data. In cross-validation, the training data is partitioned. The model is trained using all but one of the partitions, and tested on the remaining partition. The partitions are then rotated several times so that the model is trained and evaluated on all of the data. The mean of the model's scores on each of the partitions is a better estimate of performance in the real world than an evaluation using a single training/testing split. The following diagram depicts cross validation with five partitions, or folds.

The original dataset is partitioned into five subsets of equal size labeled A through E. Initially the model is trained on partitions B through E, and tested on partition A. In the next iteration, the model is trained on partitions A, C, D, and E, and tested on partition B. The partitions are rotated until models have been trained and tested on all of the partitions. Cross-validation provides a more accurate estimate of the model's performance than testing a single partition of the data.

Many metrics can be used to measure whether or not a program is learning to perform its task more effectively. For supervised learning problems, many performance metrics measure the amount of prediction error. There are two fundamental causes of prediction error: a model's bias, and its variance. Assume that you have many training sets that are all unique, but equally representative of the population. A model with high bias will produce similar errors for an input regardless of the training set it used to learn; the model biases its own assumptions about the real relationship over the relationship demonstrated in the training data. A model with high variance, conversely, will produce different errors for an input depending on the training set that it used to learn. A model with high bias is inflexible, but a model with high variance may be so flexible that it models the noise in the training set. That is, a model with high variance over-fits the training data, while a model with high bias under-fits the training data. It can be helpful to visualize bias and variance as darts thrown at a dartboard. Each dart is analogous to a prediction, and is thrown by a model trained on a different dataset every time. A model with high bias but low variance will throw darts that will be tightly clustered, but could be far from the bulls-eye. A model with high bias and high variance will throw darts all over the board; the darts are far from the bulls-eye and from each other. A model with low bias and high variance will throw darts that could be poorly clustered but close to the bulls-eye. Finally, a model with low bias and low variance will throw darts that are tightly clustered around the bulls-eye.

Ideally, a model will have both low bias and variance, but efforts to decrease one will frequently increase the other. This is known as the bias-variance trade-off. We will discuss the biases and variances of many of the models introduced in this book.

Unsupervised learning problems do not have an error signal to measure; instead, performance metrics for unsupervised learning problems measure some attribute of the structure discovered in the data, such as the distances within and between clusters.

Most performance measures can only be calculated for a specific type of task, like classification or regression. Machine learning systems should be evaluated using performance measures that represent the costs associated with making errors in the real world. While this may seem obvious, the following example describes this using a performance measure that is appropriate for the task in general but not for its specific application.

Consider a classification task in which a machine learning system observes tumors and must predict whether they are malignant or benign. Accuracy, or the fraction of instances that were classified correctly, is an intuitive measure of the program's performance. While accuracy does measure the program's performance, it does not differentiate between malignant tumors that were classified as being benign, and benign tumors that were classified as being malignant. In some applications, the costs associated with all types of errors may be the same. In this problem, however, failing to identify malignant tumors is likely a more severe error than mistakenly classifying benign tumors as being malignant.

We can measure each of the possible prediction outcomes to create different views of the classifier's performance. When the system correctly classifies a tumor as being malignant, the prediction is called a true positive. When the system incorrectly classifies a benign tumor as being malignant, the prediction is a false positive. Similarly, a false negative is an incorrect prediction that the tumor is benign, and a true negative is a correct prediction that a tumor is benign. Note that positive and negative are used only as binary labels, and are not meant to judge the phenomena they signify. In this example, it does not matter whether malignant tumors are coded as positive or negative, so long as they are coded consistently. True and false positives and negatives can be used to calculate several common measures of classification performance, including accuracy, precision and recall.

Accuracy is calculated with the following formula, where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives:

Precision is the fraction of the tumors that were predicted to be malignant that are actually malignant. Precision is calculated with the following formula:

Recall is the fraction of malignant tumors that the system identified. Recall is calculated with the following formula:

In this example, precision measures the fraction of tumors that were predicted to be malignant that are actually malignant. Recall measures the fraction of truly malignant tumors that were detected.

The precision and recall measures could reveal that a classifier with impressive accuracy actually fails to detect most of the malignant tumors. If most tumors in the testing set are benign, even a classifier that never predicts malignancy could have high accuracy. A different classifier with lower accuracy and higher recall might be better suited to the task, since it will detect more of the malignant tumors.

Many other performance measures for classification can be used. We will discuss more metrics, including metrics for multi-label classification problems, in later chapters. In the next chapter we will discuss some common performance measures for regression tasks. Performance on unsupervised tasks can also be assessed; we will discuss some performance measures for cluster analysis later in the book.

Since its release in 2007, scikit-learn has become one of the most popular machine learning libraries. scikit-learn provides algorithms for machine learning tasks including classification, regression, dimensionality reduction, and clustering. It also provides modules for pre-processing data, extracting features, optimizing hyperparameters, and evaluating models.

scikit-learn is built on the popular Python libraries NumPy and SciPy. NumPy extends Python to support efficient operations on large arrays and multi-dimensional matrices. SciPy provides modules for scientific computing. The visualization library matplotlib is often used in conjunction with scikit-learn.

scikit-learn is popular for academic research because its API is well-documented, easy-to-use, and versatile. Developers can use scikit-learn to experiment with different algorithms by changing only a few lines of code. scikit-learn wraps some popular implementations of machine learning algorithms, such as LIBSVM and LIBLINEAR. Other Python libraries, including NLTK, include wrappers for scikit-learn. scikit-learn also includes a variety of datasets, allowing developers to focus on algorithms rather than obtaining and cleaning data.

Licensed under the permissive BSD license, scikit-learn can be used in commercial applications without restrictions. Many of scikit-learn's algorithms are fast and scalable to all but massive datasets. Finally, scikit-learn is noted for its reliability; much of the library is covered by automated tests.

This book was written for version 0.18.1 of scikit-learn; use this version to ensure that the examples run correctly. If you have previously installed scikit-learn, you can retrieve the version number by executing the following in a notebook or Python interpreter:

# In[1]:
import sklearn 
sklearn.__version__ 

# Out[1]:
'0.18.1'

If you have not previously installed scikit-learn, you may install it from a package manager or build it from source. We will review the installation processes for Ubuntu 16.04, Max OS, and Windows 10 in the following sections, but refer to http://scikit-learn.org/stable/install.html for the latest instructions. The following instructions assume only that you have installed Python >= 2.6 or Python >= 3.3. See http://www.python.org/download/ for instructions on installing Python.

The easiest way to install scikit-learn is to use pip, the PyPA-recommended tool for installing Python packages. Install scikit-learn using pip as follows:

$ pip install -U scikit-learn

If pip is not available on your system, consult the following sections for installation instructions for various platforms.

scikit-learn requires setuptools, a third-party package that supports packaging and installing software for Python. Setuptools can be installed on Windows by running the bootstrap script at https://bitbucket.org/pypa/setuptools/raw/bootstrap/ez_setup.py.

Windows binaries for the 32-bit and 64-bit versions of scikit-learn are also available. If you cannot determine which version you need, install the 32-bit version. Both versions depend on NumPy 1.3 or newer. The 32-bit version of NumPy can be downloaded from http://sourceforge.net/projects/numpy/files/NumPy/. The 64-bit version can be downloaded from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn.

A Windows installer for the 32-bit version of scikit-learn can be downloaded from http://sourceforge.net/projects/scikit-learn/files/. An installer for the 64-bit version of scikit-learn can be downloaded from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn.

scikit-learn can be installed on Ubuntu 16.04 using apt.

$ sudo apt install python-scikits-learn

scikit-learn can be installed on OS X using Macports.

$ sudo port install py27-sklearn

Anaconda is a free collection of more than 720 open source data science packages for Python including scikit-learn, NumPy, SciPy, pandas, and matplotlib. Anaconda is platform-agnostic and simple to install. See https://docs.continuum.io/anaconda/install/ for instructions for your operating system.

To verify that scikit-learn has been installed correctly, open a Python console and execute the following:

# In[1]:
import sklearn 
sklearn.__version__ 

# Out[1]:
'0.18.1'

To run scikit-learn's unit tests, first install the nose Python library. Then execute the following in a terminal emulator:

$ nosetest sklearn -exe  

Congratulations! You've successfully installed scikit-learn.

pandas is an open source library that provides data structures and analysis tools for Python. pandas is a powerful library, and several books describe how to use pandas for data analysis. We will use a few of pandas's convenient tools for importing data and calculating summary statistics. Pillow is a fork of the Python Imaging Library, which provides a variety of image processing features. NLTK is a library for working with human language. As for scikit-learn, pip is the preferred installation method for pandas, Pillow, and NLTK. Execute the following command in a terminal emulator:

$ pip install pandas pillow nltk

Matplotlib is a library for easily creating plots, histograms, and other charts with Python. We will use it to visualize training data and models. Matplotlib has several dependencies. Like pandas, matplotlib depends on NumPy, which should already be installed. On Ubuntu 16.04, matplotlib and its dependencies can be installed with:

$ sudo apt install python-matplotlib  

Binaries for Mac OS and Windows 10 can be downloaded from http://matplotlib.org/downloads.html.

In this chapter, we defined machine learning as the design of programs that can improve their performance at a task by learning from experience. We discussed the spectrum of supervision in experience. At one end is supervised learning, in which a program learns from inputs that are labeled with their corresponding outputs. Unsupervised learning, in which the program must discover structure in only unlabeled inputs, is at the opposite end of the spectrum. Semi-supervised approaches make use of both labeled and unlabeled training data.

Next we discussed common types of machine learning tasks and reviewed examples of each. In classification tasks the program predict the value of a discrete response variable from the observed explanatory variables. In regression tasks the program must predict the value of a continuous response variable from the explanatory variables. Unsupervised learning tasks include clustering, in which observations are organized into groups according to some similarity measure, and dimensionality reduction, which reduces a set of explanatory variables to a smaller set of synthetic features that retain as much information as possible. We also reviewed the bias-variance trade-off and discussed common performance measures for different machine learning tasks.

In this chapter we discussed the history, goals, and advantages of scikit-learn. Finally, we prepared our development environment by installing scikit-learn and other libraries that are commonly used in conjunction with it. In the next chapter we will discuss a simple model for regression tasks, and build our first machine learning model with scikit-learn.