validation_split=02

The parameter "validation_split=0.2" is commonly used in machine learning algorithms to split the dataset into a training set and a validation set. In this case, 20% of the data is reserved for validation, and the remaining 80% is used for training.

The purpose of using a validation set is to evaluate the performance of the model on data that it has not seen during training. This helps to prevent overfitting, which is when the model becomes too complex and fits the training data too closely, resulting in poor performance on new, unseen data.

By having a separate validation set, the model can be trained on the training set and evaluated on the validation set, allowing the researcher to make adjustments to the model's parameters or architecture to improve its performance before testing it on a completely new, unseen dataset