# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 6 Evaluation Results
To confirm our research hypothesis, the results of all the five classifiers were compared with the baseline accuracy. In machine learning, the baseline model represents the simplest possible model. In classification tasks this is the proportion of the class with most observations. This algorithm is also commonly known as \"ZeroR\" or \"0-R\" algorithm, and its accuracy is used as point of reference when comparing performance of other machine learning algorithms. In regression, this can be one of the central tendency statistics. In our case, the baseline accuracy was the proportion of the major class in a data set, that is, the second class or \"medium SEO\". The majority class was observed in 293 out of 600 instances, which accounts for 48.83% of the data set. In 10-folds' cross validation, the average accuracy of all the five classifiers was above this threshold, which is shown in the box plots in Figure 3. The red line in Figure 3 represents baseline accuracy (48.83%). The central line in the boxes represents the median and the dot represents the average. The median (and average) values for all the classifiers are above the red line, which, according to [63], indicates confirmation of the set hypothesis: The classification models built for classification of web pages according to their SEO optimization level had a greater accuracy than classification in the majority class. Nevertheless, the ultimate testing of this hypothesis needs to be performed by means of statistical tests to provide statistically significant conclusions. For comparing more classifiers on one data set, parametric and non-parametric tests [64] for dependent samples can be used. The most common parametric test is \(t\)-test, which requires distribution normality. Since, in this research, meeting that requirement was mostly not possible, non-parametric tests--McNemar's and Wilcoxon were used. The McNemar test is suitable when there is a separate data set for validation (different than the training set) and is, therefore, not applicable when cross validation is used [65]. As a consequence, in this research we were able to conduct the McNemar's tests only in the hold-out validation method. This test requires building contingency tables for each pair of classifiers--in our case between classificatory and baseline accuracy. The test results are given in Table 8.  
Figure 2: Learning curve used for classification algorithms. The [PERSON] test statistic \(X^{2}\) should be greater than 3.841 (\(X^{2}\) distribution with 1 degree of freedom and \(\alpha=0.05\)) to be able to conclude with a 95% confidence that the accuracy of classification is significantly different than baseline accuracy. The tests showed that this was the case for all the tested classifiers except for Naive Bayes, which showed worse results. The Wilcoxon signed-rank test is another non-parametric test that can point out the differences (or similarities) between distributions of two samples from the same population. It is known as an alternative to the parametric \(t\)-test because it does not require a condition of normal distribution to be met. It is calculated so that first all the differences between the matching values of the variables are calculated, which are then ranked from the smallest to the largest, ignoring polarity, and summing positive and negative ranks (W\({}^{+}\) and W\({}^{-}\)). Then the minimum W = min (W\({}^{-}\), W\({}^{+}\)) is chosen, which represents the Wilcoxon value. If the number of observations (\(n\)) is smaller or equals 25 [64], then the \(p\)-value is searched in the Wilcoxon table of critical values and compared with the value of W to decide whether the null hypothesis is to be rejected. If \(n\) is larger than 25, then the normality of distribution can be assumed, and z-statistic can be calculated.