In the following 2 post I’m gonna go around the 9 most common data mining algorithms.
Inferring rudimentary rules
Here’s an easy way to find very simple classification rules from a set of instances. Called 1R for 1-rule, it generates a one-level decision tree expressed in the form of a set of rules that all test one particular attribute. The idea is this: we make rules that test a single attribute and branch accordingly. Each branch corresponds to a different value of the attribute. It is obvious what is the best classification to give each branch: use the class that occurs most often in the training data. Then the error rate of the rules can easily be determined. Just count the errors that occur on the training data, that is, the number of instances that do not have the majority class.
Missing values and numeric attributes
The good! Although a very rudimentary learning method, 1R does accommodate both missing values and numeric attributes. It deals with these in simple but effective ways. Missing is treated as just another attribute value so that, for example, if the weather data had contained missing values for the outlook attribute, a rule set formed on outlook would specify four possible class values, one each for sunny, overcast, and rainy and a fourth for missing.
this procedure tends to form a large number of categories. The 1R method will naturally gravitate toward choosing an attribute that splits into many categories, because this will partition the dataset into many classes, making it more likely that instances will have the same class as the majority in their partition. This phenomenon is known as overfitting. For 1R, overfitting is likely to occur whenever an attribute has a large number of possible values
Surprisingly, despite its simplicity 1R did astonishingly—even embarrassingly—well in comparison with state-of-the-art learning methods, and the rules it produced turned out to be just a few percentage points less accurate, on almost all of the datasets, than the decision trees produced by a state-of-the-art decision tree induction scheme.
Another simple technique is to use all attributes and allow them to make contributions to the decision that are equally important and independent of one another, given the class.
shows a summary of the weather data obtained by counting how many times each attribute–value pair occurs with each value (yes and no) for play.
In the lower part of the table, we rewrote the same information in the form of fractions, or observed probabilities. For example, of the nine days that play is yes, outlook is sunny for two, yielding a fraction of 2/9. For play the fractions are different: they are the proportion of days that play is yes and no, respectively Now suppose we encounter a new example with the values that are shown in The first table We treat the five features in outlook, temperature, humidity, windy, and the overall likelihood that play is yes or no—as equally important, independent pieces of evidence and multiply the corresponding fractions.
likelihood of yes = 2/9 x 3/9 x 3/9 x 3/9 x 9/14 = 0.0053
likelihood of no = 3/5 x 1/5 x 4/5 x 3/5 x 5/14 = 0.0206
This method goes by the name of Naïve Bayes, because it’s based on Bayes’s rule and “naïvely” assumes independence—it is only valid to multiply probabilities when the events are independent. The assumption that attributes are independent (given the class) in real life certainly is a simplistic one.
Despite the disparaging name, Naïve Bayes works very well when tested on actual datasets, particularly when combined with some of the attribute selection procedures
One thing that can go wrong with Naïve Bayes is that if a particular attribute value does not occur in the training set in conjunction with every class value, things go badly awry. Although But the bug is easily fixed by minor adjustments to the method of calculating pro For example, the upper part of Table 4.2 shows that for play = yes, outlook is sunny for two examples, overcast for four, and rainy for three, and the lower part gives these events probabilities of 2/9, 4/9, and 3/9, respectively. Instead, we could add 1 to each numerator and compensate by adding 3 to the denominator, giving probabilities of 3/12, 5/12, and 4/12, respectively. This will ensure that an attribute value that occurs zero times receives a probability which is nonzero, albeit small. The strategy of adding 1 to each count is a standard technique called the Laplace estimator after the great eighteenth-century French mathematician Pierre Laplace. Although it works well in practice, there is no particular reason for adding 1 to the counts: we could instead choose a small constant μ and use probabilities from frequencies.
Finally, there is no particular reason for dividing m into three equal parts in the numerators: we could use
In practice, the prior probabilities make little difference provided that there are a reasonable number of training instances, and people generally just estimate frequencies using the Laplace estimator by initializing all counts to one instead of to zero.
Missing values and numeric attributes
One of the really nice things about the Bayesian formulation is that missing values are no problem at all. For example, if the value of outlook were missing in the example of Table 4.3, the calculation would simply omit this attribute, yielding Numeric values are usually handled by assuming that they have a “normal”or “Gaussian” probability distribution. Then, whereas we normalized the counts for the nominal attributes into probabilities, we calculated the mean and standard deviation for each class and each numeric attribute. Thus the mean value of temperature over the yes instances is 73, and its standard deviation is 6.2. The mean is simply the average of the preceding values, that is, the sum divided by the number of values. The standard deviation is the square root of the sample variance, which we can calculate as follows: subtract the mean from each value, square the result, sum them together, and then divide by one less than the number of values.
Naïve Bayes gives a simple approach, with clear semantics, to representing, using, and learning probabilistic knowledge. Impressive results can be achieved using it. It has often been shown that Naïve Bayes rivals, and indeed outperforms, more sophisticated classifiers on many datasets. The moral is, always try the simple things first. Repeatedly in machine learning people have eventually, after an extended struggle, obtained good results using sophisticated learning methods only to discover years later that simple methods such as 1R and Naïve
Bayes do just as well—or even better.
here are many datasets for which Naïve Bayes does not do so well, however, and it is easy to see why. Because attributes are treated as though they were completely independent, the addition of redundant ones skews the learning process.
Ian H. Witten, Eibe Frank. (1999). Data mining practical machine learning tools and techniques. Elsevier