Splitting data
When developing a machine learning model, it’s important to split the data into training, validation, and test sets; this is called data splitting. This is done to evaluate the performance of the model on new, unseen data and to prevent overfitting.
The most common method for splitting the data is the train-test split, which splits the data into two sets: the training set, which is used to train the model, and the test set, which is used to evaluate the performance of the model. The data is randomly divided into two sets, with a typical split being 80% of the data for training and 20% for testing. Using this approach the model will be trained using the majority of the data (training data) and then tested on the remaining data (test set). Using this approach, we can ensure that the model’s performance is based on new, unseen data.
Most of the time in machine learning model development, we have a set of hyperparameters for our model that we like to...
