Dataset splitting becomes necessary in ML algorithms to remove bias in training data. When parameters of a machine learning algorithm are changed to best suit the training data, the consequence is frequently an overfit algorithm that performs badly on actual test data.
The most straightforward technique to divide the modelling dataset into training and testing sets is to assign two-thirds of the data points to the former and one-third to the latter. As a result, we use the training set to train the model before applying it to the test set. We may assess our model's performance in this manner.
The reason for this is because there will not be enough data in the training dataset for the model to learn an appropriate mapping of inputs to outputs when the dataset is split into train and test sets. There will also be insufficient data in the test set to evaluate the model's performance appropriately.
Splitting a dataset can also help you figure out if your model is suffering from one of two frequent issues: underfitting or overfitting: Underfitting occurs when a model is unable to contain the relationships between variables.
The train-test split is used to estimate the performance of machine learning algorithms for prediction-based Algorithms/Applications. This method is a quick and simple procedure that allows us to compare the outcomes of our own machine learning model to those of the machine.