# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 5 Insight to When to Amalgamate DC-AI with MC-AI
### Limited Availability of Representative Data
* In setting II, we fixed the amount of training and test data but varied the parameters of the ML models. For the sake of simplicity, we name this setting as \(\beta\). * In setting III, we simultaneously changed both (the parameters and the amount of data) while computing the \(\mathcal{A}\) value. For the sake of simplicity, we name this setting as \(\gamma\). After building three different ML models, we computed \(\mathcal{A}\) for the analysis. Table 2 presents the results of the experiments. Figure 6: Frequency analysis of the target class. This schematic shows the frequency information of different category values of the target class. From the results, we can see that the three settings yielded only marginal improvements in the results. In setting \(\alpha\), we changed the training data size from 66% to 80% and computed the \(\mathcal{A}\) results using the test data (the size of the test data varied from 33% to 20%). In this setting, we used fixed values of the hyperparameter (e.g., unpruned tree, default values for both SVM and random forest parameters). The \(\mathcal{A}\) value in this setting slightly changed after varying the value of the data sizes. In the \(\beta\) setting, we fixed the training and test data sizes, but changed the parameter values of all three models. We used the best-pruned tree, the optimized values of the SVM hyperparameters (polynomial kernel function's degree, gamma, shrinking, tol, etc. ), and the hyperparameter-optimized values of random forest (number of trees, number of variables required to split the tree's node, sampling size, node complexity, tree depth, etc.). In this case, \(\mathcal{A}\) was also slightly changed. In the \(\gamma\) settings, we simultaneously varied both (hyperparameters and data size) and computed \(\mathcal{A}\). In this setting, the results were slightly better compared to the other settings. However, we observed that most ML models learned the information of one class only, and the inference for the other class was zero. The best model (e.g., random forest) in some cases was unable to even predict a single instance correctly, owing to poor learning. Therefore, this model cannot be used in some safety-critical applications (e.g., prediction and classification of cancer or COVID-19). The use of such a model can lead to negative consequences in realistic scenarios. The structure of confusion metrics obtained in three test cases is shown in Figure 7.  
As shown in Figure 7, category 1 (possibility of stroke) remained highly unlearned in most cases. If an ML model trained with such data is used in real-world cases, the possibility of accurately predicting category 1 is close to zero. In these circumstances, the sampling method used in most ML models is highly unbiased, meaning the samples drawn from the data contain the higher instances from 0 categories, and therefore, the learning and generalization of ML models are very poor. In one case (the sample size was 0.8 (80%)), we observed a difference between classes 0 and 1 of 4002, which is very high considering the size of this dataset. Since the representation from one class is extremely low, the classifier tends to ignore the samples of the minority class, leading to poor learning and generalization. How to identify and fix these types of problems using AI approaches is one of the hot topics of modern times. In the next set of experiments, we applied DC-AI to improve the learning ability of the classifiers and to fix other technical problems (low accuracy, poor sampling) that cannot be resolved in a fine-grained manner using MC-AI alone. As the main purpose of DC-AI is to improve data, therefore we developed a pipeline to inspect the data first. Figure 8 presents the pipeline of DC-AI that was employed to improve the data to address the MC-AI's