## 5 Results  
In the scenario of Figure 8, one hundred samples were taken per access point and then 50% were taken for training and the rest for testing to use cross validation. Figure 8 graphs the data that was used for training, and the data that was used for testing. In addition, the data that was correctly classified is very close to the access point to which it belonged. Therefore, the accuracy must be very high. In the following, graph samples are taken very far from the access point and the accuracy of the classifier decreases drastically, as shown in Figure 8.  
This is because there is a balance between the train data and the test data. Figure 9 shows the data obtained with a graph between the accuracy of the algorithm, and Table 3 shows the percentage of data used to test the different algorithms.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Technique**} & \multicolumn{2}{c}{**Hyper-Parameters from Skleam**} \\ \hline \multirow{4}{*}{LR} & \{'C’: 1.0, ‘class\_weight’: None, ‘dual’: False, ‘fit\_intercept’: True, ‘intercept\_scaling’: 1, ‘11\_ratio’: None, ‘max\_iter’: 100, ‘multi\_class\_’: ‘auto’, ‘n\_jobs’: None, ‘penalty’: ‘12\_’, ‘random\_state’: None, ‘solver’: ‘lbfgs’, ‘tol’: 0.0001, ‘verbose\textasci{0.}’, ‘warm\_start’: False\} \\  & \{’’priors’: None, ‘var\_smoothing’: 1e-09\} \\ \hline \multirow{4}{*}{SVM} & \{’C’: 1.0, ‘break\_ties’: False, ‘cache\_size’: 200, ‘class\_weight’: None, ‘coef’: 0.0, ‘decision\_function\_shape’: ‘ovr’, ‘degree’: 3, ‘gamma’: ‘scale’, ‘kernel’: ‘rbf’, ‘max\_iter’: -1, ‘probability’: False, ‘random\_state’: None, ‘shrinking’: True, ‘tol’: 0.001, ‘verbose’: False\} \\  & \{’algorithm’: ‘auto’, ‘leaf\_size’: 30, ‘metric’: ‘minkowski’, ‘metric\_params’: None, ‘n\_jobs’: None, ‘n\_neighbors’: 8, ‘p’: 2, ‘weights’: ‘uniform’\} \\ \hline \multirow{4}{*}{DT} & \{’ccp\_alpha’: 0.0, ‘class\_weight’: None, ‘criterion’: ‘gini’, ‘max\_depth’: None, ‘max\_features’: None, ‘max\_leaf\_nodes’: None, ‘min\_impurity\_decrease’: 0.0, ‘min\_impurity\_split’: None, ‘min\_samples\_leaf’: 1, ‘min\_samples\_split’: 2, ‘min\_weight\_fraction\_leaf’: 0.0, ‘presort’: ‘deprecated’, ‘random\_state’: None, ‘splitter’: ‘best’\} \\  & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model of hyper-parameters.  
Figure 9 and Table 3 show the relationship between the test data and accuracy. We observe from the previous graph that the algorithm that best fits the data is the decision tree, followed by naive bayes, k-nearest neighbors, logistic regression, and finally the support-vector machines.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique** & **50\%** & **40\%** & **30\%** & **20\%** \\ \hline LR & 34.74\% & 36.99\% & 35.68\% & 36.59\% \\ NB & 82.14\% & 84.15\% & 82.16\% & 86.99\% \\ SVM & 21.43\% & 16.67\% & 12.43\% & 16.26\% \\ KNN & 67.53\% & 68.70\% & 70.81\% & 75.61\% \\ DT & 95.13\% & 96.34\% & 95.14\% & 96.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of techniques against the percentage of data testing.  
Figure 8: (**A**). Split date test and train each independent data, accuracy = 88.92%. (**B**). Graph all data and class.  
In Figure 10, the graph grouped by classifier is shown and the results obtained from the least amount of data to the greatest amount of test data are also shown from right to left--purple = 20%, orange = 30%, red = 40%, and blue = 50% data test. As can be seen, the greater the amount of test data, the accuracy decreases, and in the center where the data is equally proportional, the accuracy is similar.  
Figure 10: Accuracy distribution group by technique.  
Figure 9: Percentage accuracy vs. percentage test data.