The purpose of machine learning is to teach computers to execute tasks without human intervention. An increasing number of applications such as genomics, social networking, advertising, or risk analysis generate a very large amount of data that can be analyzed or mined to extract knowledge or provide insight into a process, a customer, or an organization. Ultimately, machine learning algorithms consist of identifying and validating models to optimize a performance criterion using historical, present, and future data [1:4].
Data mining is the process of extracting or identifying patterns in a dataset.
The goal of unsupervised learning is to discover patterns of regularities and irregularities in a set of observations. The process known as density estimation in statistics is broken down into two categories: discovery of data clusters and discovery of latent factors. The methodology consists of processing input data to understand patterns similar to the natural learning process in infants or animals. Unsupervised learning does not require labeled data, and therefore, is easy to implement and execute because no expertise is needed to validate an output. However, it is possible to label the output of a clustering algorithm and use it for future classification.
The purpose of data clustering is to partition a collection of data into a number of clusters or data segments. Practically, a clustering algorithm is used to organize observations into clusters by minimizing the observations within a cluster and maximizing the observations between clusters. A clustering algorithm consists of the following steps:
- Creating a model by making an assumption on the input data.
- Selecting the objective function or goal of the clustering.
- Evaluating one or more algorithms to optimize the objective function.
Data clustering is also known as data segmentation or data partitioning.
Dimension reduction techniques aim at finding the smallest but most relevant set of features that models dataset reliability. There are many reasons for reducing the number of features or parameters in a model, from avoiding overfitting to reducing computation costs.
There are many ways to classify the different techniques used to extract knowledge from data using unsupervised learning. The following taxonomy breaks down these techniques according to their purpose, although the list is far for being exhaustive, as shown in the following diagram:
The best analogy for supervised learning is function approximation or curve fitting. In its simplest form, supervised learning attempts to extract a relation or function f x → y from a training set {x, y}. Supervised learning is far more accurate and reliable than any other learning strategy. However, a domain expert may be required to label (tag) data as a training set for certain types of problems.
Supervised machine learning algorithms can be broken into two categories:
- Generative models
- Discriminative models
In order to simplify the description of statistics formulas, we adopt the following simplification: the probability of an event X is the same as the probability of the discrete random variable X to have a value x, p(X) = p(X=x). The notation of joint probability (resp. conditional probability) becomes p(X, Y) = p(X=x, Y=y) (resp. p(X|Y)=p(X=x | Y=y).
Generative models attempt to fit a joint probability distribution, p(X,Y), of two events (or random variables), X and Y, representing two sets of observed and hidden (latent) variables x and y. Discriminative models learn the conditional probability p(Y|X) of an event or random variable Y of hidden variables y, given an event or random variable X of observed variables x. Generative models are commonly introduced through the Bayes' rule. The conditional probability of an event Y, given an event X, is computed as the product of the conditional probability of the event X, given the event Y, and the probability of the event X normalized by the probability of event Y [1:5].
The Bayes' rule is the foundation of the Naïve Bayes classifier, which is the topic of Chapter 5, Naïve Bayes Classifiers.
Contrary to generative models, discriminative models compute the conditional probability p(Y|X) directly, using the same algorithm for training and classification.
Generative and discriminative models have their respective advantages and drawbacks. Novice data scientists learn to match the appropriate algorithm to each problem through experimentation. Here is a brief guideline describing which type of models makes sense according to the objective or criteria of the project:
|
Objective |
Generative models |
Discriminative models |
|---|---|---|
|
Accuracy |
Highly dependent on the training set. |
Probability estimates tend to be more accurate. |
|
Modeling requirements |
There is a need to model both observed and hidden variables, which requires a significant amount of training. |
The quality of the training set does not have to be as rigorous as for generative models. |
|
Computation cost |
This is usually low. For example, any graphical method derived from the Bayes' rule has low overhead. |
Most algorithms rely on optimization of a convex that introduces significant performance overhead. |
|
Constraints |
These models assume some degree of independence among the model features. |
Most discriminative algorithms accommodate dependencies between features. |
We can further refine the taxonomy of supervised learning algorithms by segregating between sequential and random variables for generative models and breaking down discriminative methods as applied to continuous processes (regression) and discrete processes (classification):
Reinforcement learning is not as well understood as supervised and unsupervised learning outside the realms of robotics or game strategy. However, since the 90s, genetic-algorithms-based classifiers have become increasingly popular to solve problems that require collaboration with a domain expert. For some types of applications, reinforcement learning algorithms output a set of recommended actions for the adaptive system to execute. In its simplest form, these algorithms compute or estimate the best course of action. Most complex systems based on reinforcement learning establish and update policies that can be vetoed by an expert. The foremost challenge developers of reinforcement learning systems face is that the recommended action or policy may depend on partially observable states and how to deal with uncertainty.
Genetic algorithms are not usually considered part of the reinforcement learning toolbox. However, advanced models such as learning classifier systems use genetic algorithms to classify and reward the rules and policies.
As with the two previous learning strategies, reinforcement learning models can be categorized as Markovian or evolutionary:
This is a brief overview of machine learning algorithms with a suggested taxonomy. There are almost as many ways to introduce machine learning as there are data and computer scientists. We encourage you to browse through the list of references at the end of the book and find the documentation appropriate to your level of interest and understanding.