## 4 Results
### Gradient Boosting Analysis Using XGBoost
The Alexa ranking is used to extract four classes using the quartiles with respect to the ranking. This transforms the regression analysis to a multiclass classification problem with four classes available. The first class is for the top 25% of the websites in ranking, and the other three classes are for the intervals [0%, 25%), [25%, 50%) and [50%, 75%) of the remaining websites. The measure logLoss, or logarithmic loss, penalizes a model's inaccurate classifications. This is particularly useful for multiclass classification, in which the approach assigns a probability to each of the classes for all observations (see, e.g., [40]). As we are not expecting a binary response, the logLoss function was chosen over traditional accuracy measurements. The logLoss function is given by  
\[logLoss=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{M}y_{ij}ln\left(p_{ij}\right) \tag{7}\]  
where, \(M\) is the number of classes, \(N\) the number of observations, \(y_{ij}=\{0,\ 1\}\) indicates if observation \(i\) belongs to class \(j\), an \(p_{ij}\) the respective probability. The number of pages crawled is used to scale the related features extracted. These are all page count features for a respective semantic web technology, and the feature extracted for the rss feeds. In addition, this transformation \"scales-out\" the number of pages crawled to isolate the effect, and the importance of the semantic web features measured to ranking. In particular, the following variables are transformed by dividing with the number of pages crawled (\"pages_crawled\"), \"_html\", \"_og\", \"_twitter\", \"_rss_feeds\", \"_schema_org\", \"_other\", \"_microformats\". The parameters of machine learning models have a significant impact on model performance. As a result, in order to create an appropriate XGBoost model, the XGBoost parameters must be tuned. XGBoost has seven key parameters: boosting number (or eta), max depth, min child weight, sub sample, colsample bytree, gamma, and lambda. The number of boosting or iterations is referred to as the boosting number. The greatest depth to which a tree can grow is represented by max depth. A larger max depth indicates a higher degree of fitting, but it also indicates a higher risk of overfitting. The minimum sum of instance weight required in a child is called min child weight. The algorithm will be more conservative if min child weight is set to a large value. The subsample ratio of the training instances is referred to as subsample. Overfitting can be avoided if this option is set correctly. When constructing each tree, colsample bytree refers to the subsample ratio of features. The minimum loss reduction necessary to make a further partition on a tree leaf node is referred to as gamma. The higher the gamma, the more conservative the algorithm is. Lambda represents the L2 regularization term on weights. Additionally, increasing this value causes the model to become more conservative. We perform a grid search using the facilities of the Caret R-package [41]. We search the parameter space with the \"grid\" method, using 10-fold cross validation for a tuneLength of 30 that specifies the total number of unique combinations using the trainControl and train functions of the Caret package ([PERSON], 2008). The optimal values identified are