## 4 Implementation and Results
### Training and Validation
#### 4.4.3 Sensitivity Analysis  
In order to select parameters and define an order of importance for these parameters, we study the impact of each parameter on the accuracy of the model. For that, we fix all parameters except one; then we train multiple models with different values of that parameter. The higher is the magnitude of change in input parameter sensitivity, the higher is the importance of the parameter. Sensitivity analysis is a popular feature selection approach employed to identify the important features in a dataset. In sensitivity analysis, each input parameter is selected one at a time, and the response of the machine learning model is examined to determine the parameter's importance rank. Similar methods have been used to determine the optimum weights of parameters for the training model [52; 63; 64]. Figure 9 shows the model accuracy obtained when changing only one parameter at a time while keeping the others constant. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Parameter** & **Range** & **Explanation** \\ \hline max\_depth & 10–100, None & Maximum depth in each decision tree \\ \hline n\_estimators & 10–1000 & Number of trees \\ \hline min\_samples\_split & \(10^{-5}\)–\(5\times 10^{-1}\) & Minimum samples in each node before the node is split (fraction) \\ \hline min\_samples\_leaf & \(10^{-5}\)–\(5\times 10^{-1}\) & Minimum number of data points in a leaf node (fraction) \\ \hline max\_features & auto, sqrt & Maximum number of features considered when splitting a node \\ \hline bootstrap & true, false & Random set of data for each tree \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters and ranges used in hyperparameter tuning. Figure 8: Alert inside the oil palm plantation. We also deal with the change of the clustering period and its impacts on the precision of the model. Therefore, to check whether the clustering period changes the precision of the model or not, three different dates with the same criterion were made as tests to check: 14, 45, and 140 days. The graph represented in Figure 10 shows that the worst accuracy was in clustering patches within 45 days. The clustering within 140 days is quite good, but the best one is clustering within 14 days, where the less clustering period, the less avoiding multidrivers. The best model was achieved based on the 'all' assessment method (see Section 4.3) with an accuracy of 82% in total area. This was performed with the following parameters:  
* max_depth = 10;
* n_estimators = 100;
* min_samples_split = 10\({}^{-4}\);
* min_samples_leaf = 10\({}^{-5}\);
* max_features = 'auto';
* bootstrap = true;
* clustered within 14 days. Figure 9: Changing one parameter while keeping the others fixed. When you consider the percentage and the area of the palm, the entire dataset reveals a lot of correlation between its criteria and the threshold of 0. The second-best dataset is with a threshold value of 30% and this for percentage and palm area >=1 ha. Table 3 show the results of our training model for deforestation prediction. We measure the performance of our prediction model based on the F1 score (aka F-measure), which is a popular metric for evaluating the performance of a classification model. The F1 score calculation results in a set of different average scores (macro and weighted) in the classification report. The model showed an accuracy of 0.82 of deforestation. It also indicates an F1 score of 0.69, a precision of 0.72, a recall of 0.66 for deforestation related to oil palm, an F1 score of 0.97, a precision of 0.97, and a recall of 0.98 for deforestation related to other drivers. Results of these tests of the sensitivity analysis and the changing of the clustering period show that the importance of a criterion is the following:  
1. Area (20.2%);
2. Largest_area (19.7%);
3. Perimeter (15.7%);
4. Distance_op_min (12.5%);
5. Distance_rivers_mean (7.89%);
6. Distance_road_max (7.74%);
7. Distances_patches (5.58%);
8. Distance_concession (4.59%). Figure 10: Accuracy vs. clustering thresholds.