Data Validation for Machine Learning - KDnuggets

Data is the sustenance that keeps machine learning going. No matter how powerful a machine learning and/or deep learning model is, it can never do what we want it to do with bad data. While the validation process cannot directly find what is wrong, the process can show us sometimes that there is a problem with the stability of the model. A typical ratio for this might be 80/10/10 to make sure you still have enough training data. After training the model with the training set, the user will move onto validating the results and tuning the hyperparameters with the validation set till the user reaches a satisfactory performance metric.