In this excerpt from Data Mining: Know it All, find out about the difference between machine learning and statistics, learn how and how generalization as search can be a tool for thinking about machine learning and also find out about the bias of the search, including information on language bias, search bias and overfitting-avoidance bias.
Table of contents:
An introduction to data mining
Simple data mining examples and datasets
Fielded applications of data mining and machine learning
The difference between machine learning and statistics in data mining
Information and examples on data mining and ethics
Data acquisition and integration techniques
What is a data rollup?
Calculating mode in data mining projects
Using data merging and concatenation techniques to integrate data
1.4 Machine Learning and Statistics
What's the difference between machine learning and statistics? Cynics, looking wryly at the explosion of commercial interest (and hype) in this area, equate data mining to statistics plus marketing. In truth, you should not look for a dividing line between machine learning and statistics because there is a continuum — and a multidimensional one at that — of data analysis techniques. Some derive from the skills taught in standard statistics courses, and others are more closely associated with the kind of machine learning that has arisen out of computer science. Historically, the two sides have had rather different traditions. If forced to point to a single difference of emphasis, it might be that statistics has been more concerned with testing hypotheses, whereas machine learning has been more concerned with formulating the process of generalization as a search through possible hypotheses. But this is a gross oversimplification: statistics is far more than hypothesis testing, and many machine learning techniques do not involve any searching at all.
In the past, similar methods have developed in parallel in machine learning and statistics. One is decision tree induction. Four statisticians (Breiman et al. 1984) published a book, Classification and Regression Trees, in the mid-1980s, and throughout the 1970s and early 1980s a prominent machine learning researcher, J. Ross Quinlan, was developing a system for inferring classification trees from examples. These two independent projects produced similar methods for generating trees from examples, and the researchers only became aware of one another's work much later. A second area in which similar methods have arisen involves the use of nearest-neighbor methods for classification. These are standard statistical techniques that have been extensively adapted by machine learning researchers, both to improve classification performance and to make the procedure more efficient computationally.
But now the two perspectives have converged. The techniques we will examine in this book incorporate a great deal of statistical thinking. From the beginning, when constructing and refining the initial example set, standard statistical methods apply: visualization of data, selection of attributes, discarding outliers, and so on. Most learning algorithms use statistical tests when constructing rules or trees and for correcting models that are "overfitted," in that they depend too strongly on the details of the particular examples used to produce them (we have already seen an example of this in the two decision trees of Figure 1.3 for the labor negotiations problem). Statistical tests are used to validate machine learning models and to evaluate machine learning algorithms. In our study of practical techniques for data mining, we will learn a great deal about statistics.
1.5 Generalization as Search
One way of visualizing the problem of learning — and one that distinguishes it from statistical approaches — is to imagine a search through a space of possible concept descriptions for one that fits the data. Although the idea of generalization as search is a powerful conceptual tool for thinking about machine learning, it is not essential for understanding the practical methods described here. That is why this section is considered optional.
Suppose, for definiteness, that concepts — the result of learning — are expressed as rules such as the ones given for the weather problem in Section 1.2 (although other concept description languages would do just as well). Suppose that we list all possible sets of rules and then look for ones that satisfy a given set of examples. A big job? Yes. An infinite job? At first glance it seems so because there is no limit to the number of rules there might be. But actually the number of possible rule sets is finite. Note first that each individual rule is no greater than a fixed maximum size, with at most one term for each attribute: for the weather data of Table 1.2 this involves four terms in all. Because the number of possible rules is finite, the number of possible rule sets is finite, too, although extremely large. However, we'd hardly be interested in sets that contained a very large number of rules. In fact, we' d hardly be interested in sets that had more rules than there are examples because it is difficult to imagine needing more than one rule for each example. So if we were to restrict consideration to rule sets smaller than that, the problem would be substantially reduced, although still very large.
The threat of an infinite number of possible concept descriptions seems more serious for the second version of the weather problem in Table 1.3 because these rules contain numbers. If they are real numbers, you can't enumerate them, even in principle. However, on reflection, the problem again disappears because the numbers really just represent breakpoints in the numeric values that appear in the examples. For instance, consider the temperature attribute in Table 1.3 . It involves the numbers 64, 65, 68, 69, 70, 71, 72, 75, 80, 81, 83, and 85 — 12 different numbers. There are 13 possible places in which we might want to put a breakpoint for a rule involving temperature. The problem isn't infinite after all.
So the process of generalization can be regarded as a search through an enormous, but finite, search space. In principle, the problem can be solved by enumerating descriptions and striking out those that do not fit the examples presented. A positive example eliminates all descriptions that it does not match, and a negative one eliminates those it does match. With each example, the set of remaining descriptions shrinks (or stays the same). If only one is left, it is the target description — the target concept.
If several descriptions are left, they may still be used to classify unknown objects. An unknown object that matches all remaining descriptions should be classified as matching the target; if it fails to match any description, it should be classified as being outside the target concept. Only when it matches some descriptions, but not others, is there ambiguity. In this case, if the classification of the unknown object were revealed, it would cause the set of remaining descriptions to shrink because rule sets that classified the object the wrong way would be rejected.
1.5.1 Enumerating the Concept Space
Regarding it as search is a good way of looking at the learning process. However, the search space, although finite, is extremely big, and it is generally impractical to enumerate all possible descriptions and then see which ones fit. In the weather problem there are 4 × 4 × 3 × 3 × 2 = 288 possibilities for each rule. There are four possibilities for the outlook attribute: sunny, overcast, rainy, or it may not participate in the rule at all. Similarly, there are four for temperature, three for weather and humidity, and two for the class. If we restrict the rule set to contain no more than 14 rules (because there are 14 examples in the training set), there are around 2.7 × 10 34 possible different rule sets. That's a lot to enumerate, especially for such a patently trivial problem.
Although there are ways of making the enumeration procedure more feasible, a serious problem remains: in practice, it is rare for the process to converge on a unique acceptable description. Either many descriptions are still in the running after the examples are processed or the descriptors are all eliminated. The first case arises when the examples are not sufficiently comprehensive to eliminate all possible descriptions except for the "correct" one. In practice, people often want a single "best" description, and it is necessary to apply some other criteria to select the best one from the set of remaining descriptions. The second problem arises either because the description language is not expressive enough to capture the actual concept or because of noise in the examples. If an example comes in with the "wrong" classification because of an error in some of the attribute values or in the class that is assigned to it, this will likely eliminate the correct description from the space. The result is that the set of remaining descriptions becomes empty. This situation is very likely to happen if the examples contain any noise at all, which inevitably they do except in artificial situations.
Another way of looking at generalization as search is to imagine it, not as a process of enumerating descriptions and striking out those that don't apply, but as a kind of hill-climbing in description space to find the description that best matches the set of examples according to some prespecified matching criterion. This is the way that most practical machine learning methods work. However, except in the most trivial cases, it is impractical to search the whole space exhaustively; most practical algorithms involve heuristic search and cannot guarantee to find the optimal description.
Viewing generalization as a search in a space of possible concepts makes it clear that the following are most important decisions in a machine learning system.
- The concept description language.
- The order in which the space is searched.
- The way that overfitting to the particular training data is avoided.
These three properties are generally referred to as the bias of the search and are called language bias, search bias, and overfitting-avoidance bias. You bias the learning scheme by choosing a language in which to express concepts, by searching in a particular way for an acceptable description, and by deciding when the concept has become so complex that it needs to be simplified.
The most important question for language bias is whether the concept description language is universal, or whether it imposes constraints on what concepts can be learned. If you consider the set of all possible examples, a concept is really just a division of it into subsets. In the weather example, if you were to enumerate all possible weather conditions, the play concept is a subset of possible weather conditions. A "universal" language is one that is capable of expressing every possible subset of examples. In practice, the set of possible examples is generally huge, and in this respect our perspective is a theoretic, not a practical, one.
If the concept description language permits statements involving logical or, that is, disjunctions, then any subset can be represented. If the description language is rule based, disjunction can be achieved by using separate rules. For example, one possible concept representation is just to enumerate the examples:
If outlook = overcast and temperature = hot and humidity = high
and windy = false then play = yes
If outlook = rainy and temperature = mild and humidity = high
and windy = false then play = yes
If outlook = rainy and temperature = cool and humidity = normal
and windy = false then play = yes
If outlook = overcast and temperature = cool and humidity = normal
and windy = true then play = yes
. . .
If none of the above then play = no
This is not a particularly enlightening concept description; it simply records the positive examples that have been observed and assumes that all the rest are negative. Each positive example is given its own rule, and the concept is the disjunction of the rules. Alternatively, you could imagine having individual rules for each of the negative examples, too — an equally uninteresting concept. In either case, the concept description does not perform any generalization; it simply records the original data.
On the other hand, if disjunction is not allowed, some possible concepts — sets of examples — may not be able to be represented at all. In that case, a machine learning scheme may simply be unable to achieve good performance.
Another kind of language bias is that obtained from knowledge of the particular domain being used. For example, it may be that some combinations of attribute values can never happen. This would be the case if one attribute implied another. We saw an example of this when considering the rules for the soybean problem described earlier. Then, it would be pointless to even consider concepts that involved redundant or impossible combinations of attribute values. Domain knowledge can be used to cut down the search space. Knowledge is power: a little goes a long way, and even a small hint can reduce the search space dramatically.
In realistic data mining problems, there are many alternative concept descriptions that fit the data, and the problem is to find the "best" one according to some criterion — usually simplicity. We use the term fit in a statistical sense; we seek the best description that fits the data reasonably well. Moreover, it is often computationally infeasible to search the whole space and guarantee that the description found really is the best. Consequently, the search procedure is heuristic, and no guarantees can be made about the optimality of the final result. This leaves plenty of room for bias: different search heuristics bias the search in different ways.
For example, a learning algorithm might adopt a "greedy" search for rules by trying to find the best rule at each stage and adding it in to the rule set. However, it may be that the best pair of rules is not just the two rules that are individually found to be the best. Or when building a decision tree, a commitment to split early on using a particular attribute might turn out later to be ill considered in light of how the tree develops below that node. To get around these problems, a beam search could be used in which irrevocable commitments are not made but instead a set of several active alternatives — whose number is the beam width — are pursued in parallel. This will complicate the learning algorithm considerably but has the potential to avoid the myopia associated with a greedy search. Of course, if the beam width is not large enough, myopia may still occur. There are more complex search strategies that help to overcome this problem.
A more general and higher-level kind of search bias concerns whether the search is done by starting with a general description and refining it or by starting with a specific example and generalizing it. The former is called a general-to-specific search bias, the latter a specific-to-general one. Many learning algorithms adopt the former policy, starting with an empty decision tree, or a very general rule, and specializing it to fit the examples. However, it is perfectly possible to work in the other direction. Instance-based methods start with a particular example and see how it can be generalized to cover nearby examples in the same class.
Overfitting-avoidance bias is often just another kind of search bias. But because it addresses a rather special problem, we treat it separately. Recall the disjunction problem described previously. The problem is that if disjunction is allowed, useless concept descriptions that merely summarize the data become possible, whereas if it is prohibited, some concepts are unlearnable. To get around this problem, it is common to search the concept space starting with the simplest concept descriptions and proceeding to more complex ones: simplest-first ordering. This biases the search toward simple concept descriptions.
Using a simplest-first search and stopping when a sufficiently complex concept description is found is a good way of avoiding overfitting. It is sometimes called forward pruning or prepruning because complex descriptions are pruned away before they are reached. The alternative, backward pruning or postpruning, is also viable. Here, we first find a description that fits the data well and then prune it back to a simpler description that also fits the data. This is not as redundant as it sounds: often the only way to arrive at a simple theory is to find a complex one and then simplify it. Forward and backward pruning are both a kind of overfitting-avoidance bias.
In summary, although generalization as search is a nice way to think about the learning problem, bias is the only way to make it feasible in practice. Different learning algorithms correspond to different concept description spaces searched with different biases. This is what makes it interesting: different description languages and biases serve some problems well and other problems badly. There is no universal "best" learning method — as every teacher knows!
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