As was mentioned before, there are several methods for identifying the most informative feature for a decision stump. One popular alternative, called information gain, measures how much more organized the input values become when we divide them up using a given feature. To measure how disorganized the original set of input values are, we calculate entropy of their labels, which will be high if the input values have highly varied labels, and low if many input values all have the same label. In particular, entropy is defined as the sum of the probability of each label times the log probability of that same label:
For example, Figure 6-5 shows how the entropy of labels in the name gender prediction task depends on the ratio of male to female names. Note that if most input values have the same label (e.g., if P(male) is near 0 or near 1), then entropy is low. In particular, labels that have low frequency do not contribute much to the entropy (since P(l) is small), and labels with high frequency also do not contribute much to the entropy (since log2P(l) is small). On the other hand, if the input values have a wide variety of labels, then there are many labels with a "medium" frequency, where neither P(l) nor log2P(l) is small, so the entropy is high. Example 6-8 demonstrates how to calculate the entropy of a list of labels.
Figure 6-5. The entropy of labels in the name gender prediction task, as a function of the percentage of names in a given set that are male.
Example 6-8. Calculating the entropy of a list of labels.
import math def entropy(labels):
freqdist = nltk.FreqDist(labels)
probs = [freqdist.freq(l) for l in nltk.FreqDist(labels)] return -sum([p * math.log(p,2) for p in probs])
>>> print entropy(['male', 'male', 'male', 'male']) 0.0
>>> print entropy(['male', 'female', 'male', 'male']) 0.811278124459
>>> print entropy(['female', 'male', 'female', 'male']) 1.0
>>> print entropy(['female', 'female', 'male', 'female']) 0.811278124459
>>> print entropy(['female', 'female', 'female', 'female']) 0.0
Once we have calculated the entropy of the labels of the original set of input values, we can determine how much more organized the labels become once we apply the decision stump. To do so, we calculate the entropy for each of the decision stump's leaves, and take the average of those leaf entropy values (weighted by the number of samples in each leaf). The information gain is then equal to the original entropy minus this new, reduced entropy. The higher the information gain, the better job the decision stump does of dividing the input values into coherent groups, so we can build decision trees by selecting the decision stumps with the highest information gain.
Another consideration for decision trees is efficiency. The simple algorithm for selecting decision stumps described earlier must construct a candidate decision stump for every possible feature, and this process must be repeated for every node in the constructed decision tree. A number of algorithms have been developed to cut down on the training time by storing and reusing information about previously evaluated examples.
Decision trees have a number of useful qualities. To begin with, they're simple to understand, and easy to interpret. This is especially true near the top of the decision tree, where it is usually possible for the learning algorithm to find very useful features. Decision trees are especially well suited to cases where many hierarchical categorical distinctions can be made. For example, decision trees can be very effective at capturing phylogeny trees.
However, decision trees also have a few disadvantages. One problem is that, since each branch in the decision tree splits the training data, the amount of training data available to train nodes lower in the tree can become quite small. As a result, these lower decision nodes may overfit the training set, learning patterns that reflect idiosyncrasies of the training set rather than linguistically significant patterns in the underlying problem. One solution to this problem is to stop dividing nodes once the amount of training data becomes too small. Another solution is to grow a full decision tree, but then to prune decision nodes that do not improve performance on a dev-test.
A second problem with decision trees is that they force features to be checked in a specific order, even when features may act relatively independently of one another. For example, when classifying documents into topics (such as sports, automotive, or murder mystery), features such as hasword(football) are highly indicative of a specific label, regardless of what the other feature values are. Since there is limited space near the top of the decision tree, most of these features will need to be repeated on many different branches in the tree. And since the number of branches increases exponentially as we go down the tree, the amount of repetition can be very large.
A related problem is that decision trees are not good at making use of features that are weak predictors of the correct label. Since these features make relatively small incremental improvements, they tend to occur very low in the decision tree. But by the time the decision tree learner has descended far enough to use these features, there is not enough training data left to reliably determine what effect they should have. If we could instead look at the effect of these features across the entire training set, then we might be able to make some conclusions about how they should affect the choice of label.
The fact that decision trees require that features be checked in a specific order limits their ability to exploit features that are relatively independent of one another. The naive Bayes classification method, which we'll discuss next, overcomes this limitation by allowing all features to act "in parallel."
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