The question must be plausible and meaningful in its subject area. This domain knowledge enables you to understand the things that are important in your data and to see where a certain pattern or correlation has meaning.
The question should be as specific as possible, while still giving a meaningful answer. It is common for it to begin as a generalized statement, such as "I wonder if wealthy means healthy". So, you do some further research and find you can get statistics for wealth by geographic region, say from the tax office. We can measure health through its inverse, that is, illness, say by hospital admissions, and we can test our initial proposition, "wealthy means healthy", by tying illness to geographic region. We can see that a more specific question relies on several, perhaps
questionable, assumptions.
We should also consider that our results may be confounded by the fact that poorer people may not have healthcare insurance, so are less likely to go to a hospital despite illness. There is an interaction between what we want to find out and what we are trying to measure. This interaction perhaps hides a true rate of illness. All is not lost, however. Because we know about these things, then perhaps we can account for them in our model.
We can make things a lot easier by learning as much as we can about the domain we are working in.
You could possibly save yourself a lot of time by checking whether the question you are asking, or part of it, has already been answered, or if there are data sets available that may shed some light on that topic. Often, you have to approach a problem from several different angles at once. Do as much preparatory research as you can. It is quite likely that other designers have done work that could shed light on your own.
Thinking in Machine Learning
[ 8 ]
Tasks
A task is a specific activity conducted over a period of time. We have to distinguish between the human tasks (planning, designing, and implementing) to the machine tasks (classification, clustering, regression, and so on). Also consider when there is overlap between human and machine, for example, as in selecting features for a model. Our true goal in machine learning is to transform as many of these tasks as we can from human tasks to machine tasks.
It is not always easy to match a real world problem to a specific task. Many real world problems may seem to be conceptually linked but require a very different solution. Alternatively, problems that appear completely different may require similar methods. Unfortunately, there is no simple lookup table to match a particular task to a problem. A lot depends on the setting and domain. A similar problem in one domain may be unsolvable in another, perhaps because of lack of data. There are, however, a small number of tasks that are applied to a large number of methods to solve many of the most common problem types. In other words, in the space of all possible programming tasks, there is a subset of tasks that are useful to our particular problem. Within this subset, there is a smaller subset of tasks that are easy and can actually be applied usefully to our problem.
Machine learning tasks occur in three broad settings:
• Supervised learning: The goal here is to learn a model from labeled training data that allows predictions to be made on unseen future data.
• Unsupervised learning: Here we deal with unlabeled data and our goal is to find hidden patterns in this data to extract meaningful information.
• Reinforcement learning: The goal here is to develop a system that improves its performance based on the interactions it has with its environment. This usually involves a reward signal. This is similar to supervised learning, except that rather than having a labeled training set, reinforcement learning uses a reward function to continually improve its performance.
Now, let's take a look at some of the major machine learning tasks. The following diagram should give you a starting point to try and decide what type of task is appropriate for different machine learning problems:
Chapter 1
Classification is probably the most common type of task; this is due in part to the fact that it is relatively easy, well understood, and solves a lot of common problems.
Classification is about assigning classes to a set of instances, based on their features.
This is a supervised learning method because it relies on a labeled training set to learn a set of model parameters. This model can then be applied to unlabeled data to make a prediction on what class each instance belongs to. There are broadly two types of classification tasks: binary classification and multiclass classification. A typical binary classification task is e-mail spam detection. Here we use the contents of an e-mail to determine if it belongs to one of the two classes: spam or not spam.
An example of multiclass classification is handwriting recognition, where we try to predict a class, for example, the letter name. In this case, we have one class for each of the alpha numeric characters. Multiclass classification can sometimes be achieved by chaining binary classification tasks together, however, we lose information this way, and we are unable to define a single decision boundary. For this reason, multiclass classification is often treated separately from binary classification.
Regression
There are cases where what we are interested in are not discrete classes, but a
continuous variable, for instance, a probability. These types of problems are regression problems. The aim of regression analysis is to understand how changes to the input, independent variables, effect changes to the dependent variable. The simplest
regression problems are linear and involve fitting a straight line to a set of data in order to make a prediction. This is usually done by minimizing the sum of squared errors
Thinking in Machine Learning
[ 10 ]
Clustering
Clustering is the most well known unsupervised method. Here, we are concerned with making a measurement of similarity between instances in an unlabeled dataset.
We often use geometric models to determine the distance between instances, based on their feature values. We can use an arbitrary measurement of closeness to determine what cluster each instance belongs to. Clustering is often used in data mining and exploratory data analysis. There are a large variety of methods and algorithms that perform this task, and some of the approaches include the distance-based method, as well as finding a center point for each cluster, or using statistical techniques based on distributions.
Related to clustering is association; this is an unsupervised task to find a certain type of pattern in the data. This task is behind product recommender systems such as those provided by Amazon and other on-line shops.