# Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
## 3 Results
### Machine Learning Models for Potato Variety Tolerance to Soil Drought Groups Classification
The proposed model, using selected features from the analyzed measurements (\(X\)), should allow classifying the potato varieties to assign them to groups of their tolerance to soil drought (\(y\)). To achieve this goal, several machine learning algorithms were tested, including the Quadratic Discriminant Analysis, Random Forest, Extra Trees, Ada Boost, and extreme gradient boosting. The other tested model architectures with lower performance were dropped from the analysis. The measurement dataset was split into two parts: the training set (\(n=383\)), which was used to prepare the models, and the test set (\(n=96\)), which was used to evaluate the resulting models' performance only. The dataset split was done using stratification according to the groups of tolerance to soil drought. As to the limitations of some of the used models that work only on data without missing values for some models, additional filtering was applied. In the following steps, several model configurations (denoted as config in Table 5) were investigated to find out the optimal features setup. At first, the performance of the models trained on features not related to yielding was checked. Among a few tested configurations, the best results were noted for the model using Leaves mass, Climatic water balance, Maturity, Assimilation area, and Stem mass (config 1), for which the resulting accuracy, calculated on the test set, was 72.3%. This result was obtained for the implementation of the Extra Trees Classifier, and the other architectures paid off with significantly lower accuracy. All those results are presented in Table 5. An additional check was performed to find out which of the features had the greatest impact on classification (Table 6), and it was clarified that, with the importance over 40%, this parameter was the climatic water balance. The model group that included the parameter yield-related measurements (like the Relative yield decrease or Yield per plant) returned significantly higher accuracies. The model that was trained on features config 2, with the following measured parameters: Relative yield decrease, Leaves mass, Climatic water balance, Yield per plant, and Maturity, the calculated classification accuracy was 87.1%, with a precision over 90% at the same time. However, the highest classification performance was obtained for the models that took into account the designated indicators DSI and MSTI during the training process. Each of such configurations (config 3, config 4, and config 5) that were incrementally expanded with new features is described with a detailed configuration of the features and the resulting performance in Table 5. The final implementation of the extreme boosting classifier reached the accuracy of 96.7% while maintaining high precision and recall metrics. This result was achieved by processing the measurements of the following parameters: DSI, MSTI, Relative yield decrease, Leaves mass, Climatic water balance, Yield per plant, Maturity, Assimilation area, and Stem mass. The significantly higher score, in contrast to other model architectures, was achieved due to the ability of training on samples with partially missing data (thus, an unreduced training set). The final classifier for the groups of potato varieties' tolerance to soil drought, which uses extreme boosting, is a form of the extended decision trees method, and its eventual answer is based on an analysis of several features. Figure 4 allows one to investigate how this classifying decision is taken. The presented graph, generated using the method presented in Reference [35], allows one to follow what is the individual input feature's impact on the model output regarding those feature values. In addition to the feature importance values for the final model, which are listed in Table 6, this method lets one deeper interpret the model features.