Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation

When learning a dependence from data, to avoid overfitting, it is important to divide the data into the training set and the testing set. We first train our model on the training set, and then we use the data from the testing set to gauge the accuracy of the resulting model. Empirical studies show that the best results are obtained if we use 20-30% of the data for testing, and the remaining 70-80% of the data for training. In this paper, we provide a possible explanation for this empirical result. 1 Formulation of the Problem Training a model: a general problem. In many practical situations, we have a model for a physical phenomenon, a model that includes several unknown parameters. These parameters need to be determined from the known observations; this determination is known as training the model. Need to divide data into training set and testing set. In statistics in general, the more data points we use, the more accurate are the resulting estimates. From this viewpoint, it may seem that the best way to determine the parameters of the model is to use all the available data points in this determination. This is indeed a good idea if we are absolutely certain that our model adequately describes the corresponding phenomenon. In practice, however, we are often not absolutely sure that the current model is indeed adequate. In such situations, if we simply use all the available data to determine the parameters of the model, we often get overfitting – when the model describes all the data perfectly well without being actually adequate. For