## 4 Experimental Results
### Experiment Results Based on Feature 2 (Elapsed Time and Scancode)  
Experiment results of training set, validation set, test set, and cross-validation using Dataset 1.  
As a result, the training set had the best score for random forest at 1.0, the rest of the models had similar score at 0.97 and above 0.90. The validation set had the worst score for linear SVC at 0.89, the rest of the models had a score at 0.96. The test set had the worst score for linear SVC at 0.90, while the rest of the models had a score at 0.95. Cross-validation had the worst score for linear SVC at 0.895, while the rest of the models had a score at 0.96.  
When comparing the dataset with the index and scancode described in Section 4.1 to the dataset with the elapsed time described in this section, the random forest increased from 0.93 to 1.0, the rest of the models increased from 0.90 to 0.91 in the training set score. In the validation set score, linear SVC increased from 0.86 to 0.89, the rest of the models increased from 0.88 to 0.96. In the test set score,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters** & \begin{tabular}{c} **Training Set** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **Validation Set** \\ **Score** \\ \end{tabular} & \
\begin{tabular}{c} **Test Set** \\ **Score** \\ \end{tabular} &
\begin{tabular}{c} **Cross-Validation** \\ **Score** \\ \end{tabular} \\ \hline KNN & n\_neighbors = 3 & 0.97 & 0.96 & 0.96 & 0.962 \\ \hline Logistic & C = 10000, & 0.97 & 0.96 & 0.95 & 0.967 \\ Regression & L2 regularization & & & & \\ \hline Linear SVC & - & 0.90 & 0.89 & 0.90 & 0.895 \\ \hline Decision tree & max\_depth = 3 & 0.97 & 0.96 & 0.95 & 0.968 \\ \hline Random Forest & n\_estimators = 14 & 1.00 & 0.96 & 0.95 & 0.969 \\ \hline Gradient & learning\_rate = 0.1, & 0.98 & 0.96 & 0.95 & 0.966 \\ Boosting & max\_depth = 2 & & & & \\ \hline SVM & C = 100 & 0.97 & 0.95 & 0.95 & 0.964 \\ \hline MLP & max\_iter = 1000, & 0.96 & 0.95 & 0.95 & 0.963 \\  & alpha = 0.00001 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiment results of training set, validation set, test set, and cross-validation using Dataset 1 with elapsed time.  
Figure 4: Performance evaluation results of cross-validation, accuracy, precision, recall, f1-score, and AUC according to Datasets 1 to 3.  
linear SVC increased from 0.21 to 0.90, and the rest of the models increased from 0.88 to 0.95. In the cross-validation score, the score 0.746 at MLP increased to the score 0.895 at linear SVC, and the rest of the models increased from 0.86 to 0.96. Therefore, the all scores of performance evaluation for datasets with the elapsed time are higher than those of datasets without the elapsed time. This indicates high performance when including the elapsed time. In other words, by utilizing a dataset with the elapsed time, the real keyboard data input by the user can be classified more effectively. Figure 5 shows the detailed performance evaluation results, such as cross-validation, accuracy, precision, recall, F1-score, and AUC according to Datasets 1 to 3 with the elapsed time.  
Specifically, Datasets 1 and 2 had low performance in linear SVC and random forest, while Dataset 3 had low performance in linear SVC, decision tree, and random forest. Moreover, the rest models had similar level of performance. Above all, all models evaluated the performance in all models for a dataset that includes the elapsed time when all datasets with elapsed time are compared to datasets without elapsed time. Consequently, feature 2 is appropriately selected to classify benign from malignant.