## 4 Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{6}{c}{**Kappa**} \\ \multicolumn{1}{c}{**Duncan**} \\ \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**N**} & \multicolumn{3}{c}{**Subset for Alpha = 0.05**} \\  & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**3**} & \multicolumn{1}{c}{**4**} \\ \hline LR & 100 & 0.4727280 & & & \\ DT-CART & 100 & & 0.4952041 & & \\ GB & 100 & & 0.5118734 & 0.5118734 & \\ XGBoost & 100 & & & 0.5229337 & 0.5229337 \\ RF & 100 & 1.000 & & & 0.5338400 \\ Sig. & & 1.000 & 0.0970000 & 0.2710000 & 0.2770000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: [PERSON]’s test on Cohe’s Kappa metric. Figure 7: Confusion matrix of the studied models. (**a**) Logistic regression. (**b**) Decision tree-CART. (**c**) Random forest. (**d**) Gradient boosting. (**e**) XGBoost. obtained in the kappa coefficient of 0.33 and 0.34 by the models indicate an acceptable agreement between the actual classification and the predicted classification [59; 60]. With respect to the sensitivity metric, we obtained the best value with the Random Forest and XGBoost models of 0.74. In our problem domain, this value indicates the probability that the model correctly classifies a teacher belonging to the dissatisfied class [61]. The value obtained from this metric is very similar to that reported by the Ref. [17] in their \"Review prognosis system to predict employees job satisfaction using deep neural network\" they obtain a value of 0.75 for satisfied employees and 0.77 for dissatisfied employees. In the study entitled \"A data mining based model for the prediction of satisfaction in the Surigao del Norte Division of the Department of Education\" presented by the Ref. [27], they obtained a value of 0.72 for the k-nearest neighbors algorithm, in the case of the Naive Bayes algorithm a value of 0.74 and for the C4.5 algorithm a value of 0.80. It is important to clarify that these results were obtained for administrative employees and not for basic education teachers. To date, there are no studies that address the prediction of basic education teacher job satisfaction with the machine learning approach. Since the test data set was not balanced, we decided to use the balanced accuracy metric, which is ideal for evaluating the performance of machine learning models on data sets with these characteristics [62; 63; 64]. In Figure 8, we can observe that the XGBoost, Random Forest and Gradient Boosting models obtain values close to 0.74, being the highest in the analyzed models. The value of this metric is similar to the results reported by the Ref. [18] where the authors compare job satisfaction prediction models for construction workers, with the CART and Neural Network algorithms, obtaining a value of 0.76 with the algorithm. of Decision Tress-CART. In the study in reference, the researchers used a data set of 280 instances with 20 independent variables. The feature selection technique for categorical variables used by the aforementioned authors is the same as that used in our study. We obtained similar values of F1-Score = 0.48 with XGBoost, Gradient Boosting, and Random Forest. Decision Trees-CART obtained a very close value of 0.47. Figure 8: Metrics obtained on the test data set. Also note in Figure 8 that the highest value of the metric called negative predictive value (NPV) was 0.94 and we obtained it with the Random Forest and XGBoost algorithms. Finally, Figure 8 also shows that Decision Trees-CART obtained a specificity value of 0.81, this suggests that this algorithm is adequate to predict class 0; that is, the satisfied teachers. Another metric, in the context of our study, that allows us to know the probability that the teacher is dissatisfied given that he was classified as such, is the positive predictive value. This value was also higher in the model built by the Decision Trees-CART algorithm. The positive predictive value was 0.38 in the model built by the Decision Trees-CART algorithm.