Evaluation of the rain-Test Split The train-test split is a technique for assessing a machine learning algorithm's performance. It can be used for any supervised learning technique and can be utilized for classification or regression tasks. The process involves partitioning a dataset into two subsets.
The key purpose behind separating the dataset into a validation set is to prevent our model from overfitting, which occurs when the model gets extremely good at identifying samples in the training set but is unable to generalize and make accurate classifications on data it has never seen before.
The benefits of splitting data in machine learning is that it helps with making more accurate predictions. It also helps with reducing the time needed for training a model as well as speeding up the process of tuning a model’s hyperparameters.