It is generally recommended to use a balanced dataset when training and testing a machine learning model, because a balanced dataset can help the model to make more accurate predictions. A balanced dataset is one where the number of observations for each class is roughly equal.

One reason for this is that a balanced dataset can help to prevent bias in the model. For example, if the dataset is heavily imbalanced in favor of one class, the model may be more likely to predict the dominant class, leading to lower accuracy for the minority class.

However, it is also important to consider the specific goals and requirements of your analysis when deciding which dataset to use. If the goal of your analysis is to maximize accuracy or minimize error, a balanced dataset may be the best choice. On the other hand, if you are more interested in maximizing a specific metric such as specificity, you may need to trade off some accuracy in order to achieve this. Ultimately, the best approach will depend on the specific context of your analysis.

DEAR Gherman Alexandru Andrei

Thanks a lot for your explaination