33 min read

In this article by Ivan Idris, Yuxi (Hayden) Liu, and Shoahoa Zhang author of the book Python Machine Learning By Example we cover basic machine learning concepts. If you need more information, then your local library, the Internet, and Wikipedia in particular should help you further.

The topics that will be covered in this article are as follows:

(For more resources related to this topic, see here.)

  • What is machine learning and why do we need it?
  • A very high level overview of machine learning
  • Generalizing with data
  • Overfitting and the bias variance trade off
  • Dimensions and features
  • Preprocessing, exploration, and feature engineering
  • Combining models
  • Installing software and setting up
  • Troubleshooting and asking for help

What is machine learning and why do we need it?

Machine learning is a term coined around 1960 composed of two words – machine corresponding to a computer, robot, or other device, and learning an activity, which most humans are good at. So we want machines to learn, but why not leave learning to the humans? It turns out that there are many problems involving huge datasets, for instance, where it makes sense to let computers do all the work. In general, of course, computers and robots don’t get tired, don’t have to sleep, and may be cheaper. There is also an emerging school of thought called active learning or human-in-the-loop, which advocates combining the efforts of machine learners and humans. The idea is that there are routine boring tasks more suitable for computers, and creative tasks more suitable for humans. According to this philosophy machines are able to learn, but not everything.

Machine learning doesn’t involve the traditional type of programming that uses business rules. A popular myth says that the majority of the code in the world has to do with simple rules possibly programmed in Cobol, which covers the bulk of all the possible scenarios of client interactions. So why can’t we just hire many coders and continue programming new rules? One reason is the cost of defining and maintaining rules becomes very expensive over time. A lot of the rules involve matching strings or numbers, and change frequently. It’s much easier and more efficient to let computers figure out everything themselves from data. Also the amount of data is increasing, and actually the rate of growth is itself accelerating. Nowadays the floods of textual, audio, image, and video data are hard to fathom. The Internet of Things is a recent development of a new kind of Internet, which interconnects everyday devices. The Internet of Things will bring data from household appliances and autonomous cars to the forefront. The average company these days has mostly human clients, but for instance social media companies tend to have many bot accounts. This trend is likely to continue and we will have more machines talking to each other.

An application of machine learning that you may be familiar is the spam filter, which filters e-mails considered to be spam. Another is online advertising where ads are served automatically based on information advertisers have collected about us. Yet another application of machine learning is search engines. Search engines extract information about web pages, so that you can efficiently search the Web. Many online shops and retail companies have recommender engines, which recommend products and services using machine learning. The list of applications is very long and also includes fraud detection, medical diagnosis, sentiment analysis, and financial market analysis.

In the 1983, War Games movie a computer made life and death decisions, which could have resulted in Word War III. As far as I know technology wasn’t able to pull off such feats at the time. However, in 1997 the Deep Blue supercomputer did manage to beat a world chess champion. In 2005, a Stanford self-driving car drove by itself for more than 130 kilometers in a desert. In 2007, the car of another team drove through regular traffic for more than 50 kilometers. In 2011, the Watson computer won a quiz against human opponents. In 2016 the AlphaGo program beat one of the best Go players in the world. If we assume that computer hardware is the limiting factor, then we can try to extrapolate into the future. Ray Kurzweil did just that and according to him, we can expect human level intelligence around 2029.

A very high level overview of machine learning

Machine learning is a subfield of artificial intelligence—a field of computer science concerned with creating systems, which mimic human intelligence. Software engineering is another field of computer science, and we can label Python programming as a type of software engineering. Machine learning is also closely related to linear algebra, probability theory, statistics, and mathematical optimization. We use optimization and statistics to find the best models, which explain our data. If you are not feeling confident about your mathematical knowledge, you probably are wondering, how much time you should spend learning or brushing up. Fortunately to get machine learning to work for you, most of the time you can ignore the mathematical details as they are implemented by reliable Python libraries. You do need to be able to program.

As far as I know if you want to study machine learning, you can enroll into computer science, artificial intelligence, and more recently, data science masters. There are various data science bootcamps; however, the selection for those is stricter and the course duration is often just a couple of months. You can also opt for the free massively online courses available on the Internet. Machine learning is not only a skill, but also a bit of sport. You can compete in several machine learning competitions; sometimes for decent cash prizes. However, to win these competitions, you may need to utilize techniques, which are only useful in the context of competitions and not in the context of trying to solve a business problem.

A machine learning system requires inputs—this can be numerical, textual, visual, or audiovisual data. The system usually has outputs, for instance, a floating point number, an integer representing a category (also called a class), or the acceleration of a self-driving car. We need to define (or have it defined for us by an algorithm) an evaluation function called loss or cost function, which tells us how well we are learning. In this setup, we create an optimization problem for us with the goal of learning in the most efficient way. For instance, if we fit data to a line also called linear regression, the goal is to have our line be as close as possible to the data points on average.

We can have unlabeled data, which we want to group or explore – this is called unsupervised learning. Unsupervised learning can be used to detect anomalies, such as fraud or defective equipment. We can also have labeled examples to use for training—this is called supervised learning. The labels are usually provided by human experts, but if the problem is not too hard, they also may be produced by any members of the public through crowd sourcing for instance. Supervised learning is very common and we can further subdivide it in regression and classification. Regression trains on continuous target variables, while classification attempts to find the appropriate class label. If not all examples are labeled, but still some are we have semi supervised learning. A chess playing program usually applies reinforcement learning—this is a type of learning where the program evaluates its own performance by, for instance playing against itself or earlier versions of itself.

We can roughly categorize machine learning algorithms in logic-based learning, statistical learning, artificial neural networks, and genetic algorithms. In fact, we have a whole zoo of algorithms with popularity varying over time. The logic-based systems were the first to be dominant. They used basic rules specified by human experts, and with these rules systems tried to reason using formal logic. In the mid-1980s, artificial neural networks came to the foreground, to be then pushed aside by statistical learning systems in the 1990s. Artificial neural networks imitate animal brains, and consist of interconnected neurons that are also an imitation of biological neurons. Genetic algorithms were pretty popular in the 1990s (or at least that was my impression). Genetic algorithms mimic the biological process of evolution. We are currently (2016) seeing a revolution in deep learning, which we may consider to be a rebranding of neural networks. The term deep learning was coined around 2006, and it refers to deep neural networks with many layers. The breakthrough in deep learning is amongst others caused by the availability of graphics processing units (GPU), which speed up computation considerably. GPUs were originally intended to render video games, and are very good in parallel matrix and vector algebra. It is believed that deep learning resembles, the way humans learn, therefore it may be able to deliver on the promise of sentient machines.

You may have heard of Moore’s law—an empirical law, which claims that computer hardware improves exponentially with time. The law was first formulated by Gordon Moore, the co-founder of Intel, in 1965. According to the law the number of transistors on a chip should double every two years. In the following graph, you can see that the law holds up nicely (the size of the bubbles corresponds to the average transistor count in GPUs):

The consensus seems to be that Moore’s law should continue to be valid for a couple of decades. This gives some credibility to Ray Kurzweil’s predictions of achieving true machine intelligence in 2029.

We will encounter many of the types of machine learning later in this book. Most of the content is about supervised learning with some examples of unsupervised learning. The popular Python libraries support all these types of learning.

Generalizing with data

The good thing about data is that we have a lot of data in the world. The bad thing is that it is hard to process this data. The challenges stem from the diversity and noisiness of the data. We as humans, usually process data coming in our ears and eyes. These inputs are transformed into electrical or chemical signals. On a very basic level computers and robots also work with electrical signals. These electrical signals are then translated into ones and zeroes. However, we program in Python in this book, and on that level normally we represent the data either as numbers, images, or text. Actually images and text are not very convenient, so we need to transform images and text into numerical values.

Especially in the context of supervised learning we have a scenario similar to studying for an exam. We have a set of practice questions and the actual exam. We should be able to answer exam questions without knowing the answers for them. This is called generalization – we learn something from our practice questions and hopefully are able to apply this knowledge to other similar questions. Finding good representative examples is not always an easy task depending on the complexity of the problem we are trying to solve and how generic the solution needs to be.

An old-fashioned programmer would talk to a business analyst or other expert, and then implement a rule that adds a certain value multiplied by another value corresponding for instance to tax rules. In a machine learning setup we can give the computer example input values and example output values. Or if we are more ambitious, we can feed the program the actual tax texts and let the machine process the data further just like an autonomous car doesn’t need a lot of human input.

This means implicitly that there is some function, for instance, a tax formula that we are trying to find. In physics we have almost the same situation. We want to know how the universe works, and formulate laws in a mathematical language. Since we don’t know the actual function all we can do is measure what our error is, and try to minimize it. In supervised learning we compare our results against the expected values. In unsupervised learning we measure our success with related metrics. For instance, we want clusters of data to be well defined. In reinforcement learning a program evaluates its moves, for example, in a chess game using some predefined function.

Overfitting and the bias variance trade off

Overfitting, (one word) is such an important concept, that I decided to start discussing it very early in this book. If you go through many practice questions for an exam, you may start to find ways to answer questions, which have nothing to do with the subject material. For instance, you may find that if you have the word potato in the question, the answer is A, even if the subject has nothing to do with potatoes. Or even worse, you may memorize the answers for each question verbatim. We can then score high on the practice questions, which are called the train set in machine learning. However, we will score very low on the exam questions, which are called the test set in machine learning. This phenomenon of memorization is called bias. Overfitting almost always means that we are trying too hard to extract information from the data (random quirks), and using more training data will not help.

The opposite scenario is called underfitting. When we underfit we don’t have a very good result on the train set, but also not a very good result on the test set. Underfitting may indicate that we are not using enough data, or that we should try harder to do something useful with the data. Obviously we want to avoid both scenarios. Machine learning errors are the sum of bias and variance. Variance measures how much the error varies.

Imagine that we are trying to decide what to wear based on the weather outside. If you have grown up in a country with a tropical climate, you may be biased towards always wearing summer clothes. If you lived in a cold country, you may decide to go to a beach in Bali wearing winter clothes. Both decisions have high bias. You may also just wear random clothes that are not appropriate for the weather outside – this is an outcome with high variance. It turns out that when we try to minimize bias or variance, we tend to increase the other – a concept known as the bias variance tradeoff. The expected error is given by the following equation:

The last term is the irreducible error.

Cross-validation

Cross-validation is a technique, which helps to avoid overfitting. Just like we would have a separation of practice questions and exam questions, we also have training sets and test sets. It’s important to keep the data separated and only use the test set in the final stage. There are many cross-validation schemes in use. The most basic setup splits the data given a specified percentage – for instance 75 % train data and 25 % test set. We can also leave out a fixed number of observations in multiple rounds, so that these items are in the test set, and the rest of the data is in the train set. For instance, we can apply leave-one-outcross-validation (LOOCV) and let each datum be in the test set once. For a large dataset LOOCV requires as many rounds as there are data items, and can therefore be too slow.

The k-fold cross-validation performs better than LOOCV and randomly splits the data into k (a positive integer) folds. One of the folds becomes the test set, and the rest of the data becomes the train set. We repeat this process k times with each fold being the designated test set once. Finally, we average the k train and test errors for the purpose of evaluation. Common values for k are five and ten. The following table illustrates the setup for five folds:

Iteration

Fold 1

Fold 2

Fold 3

Fold 4

Fold 5

1

Test

Train

Train

Train

Train

2

Train

Test

Train

Train

Train

3

Train

Train

Test

Train

Train

4

Train

Train

Train

Test

Train

5

Train

Train

Train

Train

Test

 We can also randomly split the data into train and test set multiple times. The problem with this algorithm is that some items may never end in the test set, while others may be selected in the test set multiple times. The nested cross-validation is a combination of cross-validations. Nested cross-validation consists of the following cross-validations:

  • The inner cross-validation does optimization to find the best fit, and can be implemented as k-fold cross validation.
  • The outer cross-validation is used to evaluate performance and do statistical analysis.

Regularization

Regularization like cross-validation is a general technique to fight overfitting. Regularization adds extra parameters to the error function we are trying to minimize, in order to penalize complex models. For instance, if we are trying to fit a curve to a high order polynomial, we may use regularization to reduce the influence of the higher degrees, thereby effectively reducing the order of the polynomial.

Simpler methods are to be preferred, at least according to the principle of Occam’s razor. William Occam was a monk and philosopher who around 1320 came up with the idea that the simplest hypothesis that fits data should be preferred. One justification is that you can invent fewer simple models than complex models. For instance, intuitively we know that there are more higher polynomial models than linear ones. The reason is that a line (f(x) = ax + b) is governed by only two numbers – the intercept b and slope a. The possible coefficients for a line span two-dimensional space. A quadratic polynomial adds an extra coefficient for the quadratic term, and we can span a three-dimensional space with the coefficients. Therefore it is less likely that we find a linear model than more complex models, because the search space for linear models is much smaller (although it is infinite). And of course simpler models are just easier to use and require less computation time.

We can also stop a program early as a form of regularization. If we give a machine learner less time it is more likely to produce a simpler model, and we hope less likely to overfit. Of course, we have to do this in an ordered fashion, so this means that the algorithm should be aware of the possibility of early termination.

Dimensions and features

We typically represent the data as a grid of numbers (a matrix). Each column represents a variable, which we call a feature in machine learning. In supervised learning one of the variables is actually not a feature, but the label that we are trying to predict. And in supervised learning each row is an example that we can use for training or testing. The number of features corresponds to the dimensionality of the data. Our machine learning approach depends on the number of dimensions versus the number of examples. For instance, text and image data are very high dimensional, while stock market data has relatively fewer dimensions. Fitting high dimensional data is computationally expensive, and is also prone to overfitting due to the high complexity. Higher dimensions are also impossible to visualize, and therefore we can’t use simple diagnostic methods.

Not all the features are useful and they may only add randomness to our results. It is therefore often important to do good feature selection. Feature selection is a form of dimensionality reduction. Some machine learning algorithms are actually able to automatically perform feature selection. We should be careful not to omit features that do contain information. Some practitioners evaluate the correlation of a single feature and the target variable. In my opinion you shouldn’t do that, because correlation of one variable with the target in itself doesn’t tell you much, instead use an off-the-shelf algorithm and evaluate the results of different feature subsets.

In principle, feature selection boils down to multiple binary decisions whether to include a feature or not. For n features we get 2n feature sets, which can be a very large number for a large number of features. For example, for 10 features we have 1024 possible feature sets (for instance if we are deciding what clothes to wear the features can be temperature, rain, the weather forecast, where we are going, and so on). At a certain point brute force evaluation becomes infeasible. We will discuss better methods in this book. Basically we have two options: we either start with all the features, and remove features iteratively or we start with a minimum set of features and add features iteratively. We then take the best feature sets for each iteration, and then compare them.

Another common dimensionality reduction approach is to transform high-dimensional data in lower-dimensional space. This transformation leads to information loss, but we can keep the loss to a minimum. We will cover this in more detail later on.

Preprocessing, exploration, and feature engineering

Data mining, a buzzword in the 1990 is the predecessor of data science (the science of data). One of the methodologies popular in the data mining community is called cross industry standard process for data mining (CRISP DM). CRISP DM was created in 1996, and is still used today. I am not endorsing CRISP DM, however I like its general framework. The CRISP DM consists of the following phases, which are not mutually exclusive and can occur in parallel:

  • Business understanding: This phase is often taken care of by specialized domain experts. Usually we have a business person formulate a business problem, such as selling more units of a certain product.
  • Data understanding: This is also a phase, which may require input from domain experts, however, often a technical specialist needs to get involved more than in the business understanding phase. The domain expert may be proficient with spreadsheet programs, but have trouble with complicated data. In this book I usually call this phase exploration.
  • Data preparation: This is also a phase where a domain expert with only Excel knowhow may not be able to help you. This is the phase where we create our training and test datasets. In this book I usually call this phase preprocessing.
  • Modeling: This is the phase, which most people associate with machine learning. In this phase we formulate a model, and fit our data.
  • Evaluation: In this phase, we evaluate our model, and our data to check whether we were able to solve our business problem.
  • Deployment: This phase usually involves setting up the system in a production environment (it is considered good practice to have a separate production system). Typically this is done a specialized team.

When we learn, we require high quality learning material. We can’t learn from gibberish, so we automatically ignore anything that doesn’t make sense. A machine learning system isn’t able to recognize gibberish, so we need to help it by cleaning the input data. It is often claimed that cleaning the data forms a large part of machine learning. Sometimes cleaning is already done for us, but you shouldn’t count on it. To decide how to clean the data, we need to be familiar with the data. There are some projects, which try to automatically explore the data, and do something intelligent, like producing a report. For now unfortunately we don’t have a solid solution, so you need to do some manual work.

We can do two things, which are not mutually exclusive – first scan the data and second visualizing the data. This also depends on the type of data we are dealing with whether we have a grid of numbers, images, audio, text, or something else. At the end, a grid of numbers is the most convenient form, and we will always work towards having numerical features.

We want to know if features miss values, how the values are distributed and what type of features we have. Values can approximately follow a Normal distribution, a Binomial distribution, a Poisson distribution or another distribution altogether. Features can be binary – either yes or no, positive or negative, and so on. They can also be categorical – pertaining to a category, for instance continents (Africa, Asia, Europe, Latin America, North America, and so on).Categorical variables can also be ordered – for instance high, medium, and low. Features can also be quantitative – for example temperature in degrees or price in dollars.

Feature engineering is the process of creating or improving features. It’s more of a dark art than a science. Features are often created based on common sense, domain knowledge or prior experience. There are certain common techniques for feature creation, however there is no guarantee that creating new features will improve your results. We are sometimes able to use the clusters found by unsupervised learning as extra features. Deep neural networks are often able to create features automatically.

Missing values

Quite often we miss values for certain features. This could happen for various reasons. It can be inconvenient, expensive or even impossible to always have a value. Maybe we were not able to measure a certain quantity in the past, because we didn’t have the right equipment, or we just didn’t know that the feature is relevant. However, we are stuck with missing values from the past. Sometimes it’s easy to figure out that we miss values and we can discover this just by scanning the data, or counting the number of values we have for a feature and comparing to the number of values we expect based on the number of rows. Certain systems encode missing values with for example values as 999999. This makes sense if the valid values are much smaller than 999999. If you are lucky you will have information about the features provided by whoever created the data in the form of a data dictionary or metadata.

Once we know that we miss values the question arises of how to deal with them. The simplest answer is to just ignore them. However, some algorithms can’t deal with missing values, and the program will just refuse to continue. In other circumstances ignoring missing values will lead to inaccurate results. The second solution is to substitute missing values by a fixed value – this is called imputing. We can impute the arithmetic mean, median or mode of the valid values of a certain feature. Ideally, we will have a relation between features or within a variable that is somewhat reliable. For instance, we may know the seasonal averages of temperature for a certain location and be able to impute guesses for missing temperature values given a date.

Label encoding

Humans are able to deal with various types of values. Machine learning algorithms with some exceptions need numerical values. If we offer a string such as Ivan unless we are using specialized software the program will not know what to do. In this example we are dealing with a categorical feature – names probably. We can consider each unique value to be a label. (In this particular example we also need to decide what to do with the case – is Ivan the same as ivan). We can then replace each label by an integer – label encoding. This approach can be problematic, because the learner may conclude that there is an ordering.

One hot encoding

The one-of-K or one-hot-encoding scheme uses dummy variables to encode categorical features. Originally it was applied to digital circuits. The dummy variables have binary values like bits, so they take the values zero or one (equivalent to true or false). For instance, if we want to encode continents we will have dummy variables, such as is_asia, which will be true if the continent is Asia and false otherwise. In general, we need as many dummy variables, as there are unique labels minus one. We can determine one of the labels automatically from the dummy variables, because the dummy variables are exclusive. If the dummy variables all have a false value, then the correct label is the label for which we don’t have a dummy variable. The following table illustrates the encoding for continents:

 

Is_africa

Is_asia

Is_europe

Is_south_america

Is_north_america

Africa

True

False

False

False

False

Asia

False

True

False

False

False

Europe

False

False

True

False

False

South America

False

False

False

True

False

North America

False

False

False

False

True

Other

False

False

False

False

False

 The encoding produces a matrix (grid of numbers) with lots of zeroes (false values) and occasional ones (true values). This type of matrix is called a sparse matrix. The sparse matrix representation is handled well by the SciPy package, and shouldn’t be an issue. We will discuss the SciPy package later in this article.

Scaling

Values of different features can differ by orders of magnitude. Sometimes this may mean that the larger values dominate the smaller values. This depends on the algorithm we are using. For certain algorithms to work properly we are required to scale the data. There are several common strategies that we can apply:

  • Standardization removes the mean of a feature and divides by the standard deviation. If the feature values are normally distributed, we will get a Gaussian, which is centered around zero with a variance of one.
  • If the feature values are not normally distributed, we can remove the median and divide by the interquartile range. The interquartile range is a range between the first and third quartile (or 25th and 75th percentile).
  • Scaling features to a range is a common choice of range which is a range between zero and one.

Polynomial features

If we have two features a and b, we can suspect that there is a polynomial relation, such as a2 + ab + b2. We can consider each term in the sum to be a feature – in this example we have three features. The product ab in the middle is called an interaction. An interaction doesn’t have to be a product, although this is the most common choice, it can also be a sum, a difference or a ratio. If we are using a ratio to avoid dividing by zero, we should add a small constant to the divisor and dividend. The number of features and the order of the polynomial for a polynomial relation are not limited. However, if we follow Occam’s razor we should avoid higher order polynomials and interactions of many features. In practice, complex polynomial relations tend to be more difficult to compute and not add much value, but if you really need better results they may be worth considering.

Power transformations

Power transforms are functions that we can use to transform numerical features into a more convenient form, for instance to conform better to a normal distribution. A very common transform for values, which vary by orders of magnitude, is to take the logarithm. Taking the logarithm of a zero and negative values is not defined, so we may need to add a constant to all the values of the related feature before taking the logarithm. We can also take the square root for positive values, square the values or compute any other power we like.

Another useful transform is the Box-Cox transform named after its creators. The Box-Cox transform attempts to find the best power need to transform the original data into data that is closer to the normal distribution. The transform is defined as follows:

Binning

Sometimes it’s useful to separate feature values into several bins. For example, we may be only interested whether it rained on a particular day. Given the precipitation values we can binarize the values, so that we get a true value if the precipitation value is not zero, and a false value otherwise. We can also use statistics to divide values into high, low, and medium bins.

The binning process inevitably leads to loss of information. However, depending on your goals this may not be an issue, and actually reduce the chance of overfitting. Certainly there will be improvements in speed and memory or storage requirements.

Combining models

In (high) school we sit together with other students, and learn together, but we are not supposed to work together during the exam. The reason is, of course, that teachers want to know what we have learned, and if we just copy exam answers from friends, we may have not learned anything. Later in life we discover that teamwork is important. For example, this book is the product of a whole team, or possibly a group of teams.

Clearly a team can produce better results than a single person. However, this goes against Occam’s razor, since a single person can come up with simpler theories compared to what a team will produce. In machine learning we nevertheless prefer to have our models cooperate with the following schemes:

  • Bagging
  • Boosting
  • Stacking
  • Blending
  • Voting and averaging

Bagging

Bootstrap aggregating or bagging is an algorithm introduced by Leo Breiman in 1994, which applies bootstrapping to machine learning problems. Bootstrapping is a statistical procedure, which creates datasets from existing data by sampling with replacement. Bootstrapping can be used to analyze the possible values that arithmetic mean, variance or other quantity can assume.

The algorithm aims to reduce the chance of overfitting with the following steps:

  1. We generate new training sets from input train data by sampling with replacement.
  2. Fit models to each generated training set.
  3. Combine the results of the models by averaging or majority voting.

Boosting

In the context of supervised learning we define weak learners as learners that are just a little better than a baseline such as randomly assigning classes or average values. Although weak learners are weak individually like ants, together they can do amazing things just like ants can. It makes sense to take into account the strength of each individual learner using weights. This general idea is called boosting. There are many boosting algorithms; boosting algorithms differ mostly in their weighting scheme. If you have studied for an exam, you may have applied a similar technique by identifying the type of practice questions you had trouble with and focusing on the hard problems.

Face detection in images is based on a specialized framework, which also uses boosting. Detecting faces in images or videos is a supervised learning. We give the learner examples of regions containing faces. There is an imbalance, since we usually have far more regions (about ten thousand times more) that don’t have faces. A cascade of classifiers progressively filters out negative image areas stage by stage. In each progressive stage the classifiers use progressively more features on fewer image windows. The idea is to spend the most time on image patches, which contain faces. In this context boosting is used to select features and combine results.

Stacking

Stacking takes the outputs of machine learning estimators and then uses those as inputs for another algorithm. You can, of course, feed the output of the higher-level algorithm to another predictor. It is possible to use any arbitrary topology, but for practical reasons you should try a simple setup first as also dictated by Occam’s razor.

Blending

Blending was introduced by the winners of the one million dollar Netflix prize. Netflix organized a contest with the challenge of finding the best model to recommend movies to their users. Netflix users can rate a movie with a rating of one to five stars. Obviously each user wasn’t able to rate each movie, so the user movie matrix is sparse. Netflix published an anonymized training and test set. Later researchers found a way to correlate the Netflix data to IMDB data. For privacy reasons the Netflix data is no longer available. The competition was won in 2008 by a group of teams combining their models. Blending is a form of stacking. The final estimator in blending however trains only on a small portion of the train data.

Voting and averaging

We can arrive at our final answer through majority voting or averaging. It’s also possible to assign different weights to each model in the ensemble. For averaging we can also use the geometric mean or the harmonic mean instead of the arithmetic mean. Usually combining the results of models, which are highly correlated to each other doesn’t lead to spectacular improvements. It’s better to somehow diversify the models, by using different features or different algorithms. If we find that two models are strongly correlated, we may for example decide to remove one of them from the ensemble, and increase proportionally the weight of the other model.

Installing software and setting up

For most projects in this book we need scikit-learn (Refer, http://scikit-learn.org/stable/install.html) and matplotlib (Refer, http://matplotlib.org/users/installing.html). Both packages require NumPy, but we also need SciPy for sparse matrices as mentioned before. The scikit-learn library is a machine learning package, which is optimized for performance as a lot of the code runs almost as fast as equivalent C code. The same statement is true for NumPy and SciPy. There are various ways to speed up the code, however they are out of scope for this book, so if you want to know more, please consult the documentation.

Matplotlib is a plotting and visualization package. We can also use the seaborn package for visualization. Seaborn uses matplotlib under the hood. There are several other Python visualization packages that cover different usage scenarios. Matplotlib and seaborn are mostly useful for the visualization for small to medium datasets. The NumPy package offers the ndarray class and various useful array functions. The ndarray class is an array, that can be one or multi-dimensional. This class also has several subclasses representing matrices, masked arrays, and heterogeneous record arrays. In machine learning we mainly use NumPy arrays to store feature vectors or matrices composed of feature vectors. SciPy uses NumPy arrays and offers a variety of scientific and mathematical functions. We also require the pandas library for data wrangling.

In this book we will use Python 3. As you may know Python 2 will no longer be supported after 2020, so I strongly recommend switching to Python 3. If you are stuck with Python 2 you still should be able to modify the example code to work for you. In my opinion the Anaconda Python 3 distribution is the best option. Anaconda is a free Python distribution for data analysis and scientific computing. It has its own package manager conda. The distribution includes more than 200 Python packages, which makes it very convenient. For casual users the Miniconda distribution may be the better choice. Miniconda contains the conda package manager and Python.

The procedures to install Anaconda and Miniconda are similar. Obviously, Anaconda takes more disk space. Follow the instructions from the Anaconda website at http://conda.pydata.org/docs/install/quick.html. First, you have to download the appropriate installer for your operating system and Python version. Sometimes you can choose between a GUI and a command line installer. I used the Python 3 installer, although my system Python version is 2.7. This is possible since Anaconda comes with its own Python. On my machine the Anaconda installer created an anaconda directory in my home directory and required about 900 MB. The Miniconda installer installs a miniconda directory in your home directory. Installation instructions for NumPy are at http://docs.scipy.org/doc/numpy/user/install.html. Alternatively install NumPy with pip as follows:

$ [sudo] pip install numpy

The command for Anaconda users is:

$ conda install numpy

To install the other dependencies substitute NumPy by the appropriate package. Please read the documentation carefully, not all options work equally well for each operating system. The pandas installation documentation is at http://pandas.pydata.org/pandas-docs/dev/install.html.

Troubleshooting and asking for help

Currently the best forum is at http://stackoverflow.com. You can also reach out on mailing lists or IRC chat. The following is a list of mailing lists:

IRC channels:

  •     #scikit-learn @ freenode
  •     #scipy @ freenode

Summary

In this article we covered the basic concepts of machine learning, a high level overview, generalizing with data, overfitting, dimensions and features, preprocessing, combining models, installing the required software and some places where you can ask for help.

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Further resources on this subject:

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