How to Split Data for Machine Learning Training and Evaluation

B

The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling.

When training a machine learning model, it is essential to evaluate its performance accurately. To do this, you need to split the data into training and evaluation datasets. The split ensures that the model does not learn from the evaluation data during training and can accurately generalize to new data.

Option B, "Randomly split the data into rows for training and rows for evaluation," is the correct answer. The typical split is 70% of the data for training and 30% for evaluation, but this can vary depending on the dataset size and the specific problem being addressed. The random split ensures that both the training and evaluation datasets have a representative sample of the data.

Option A, "Use features for training and labels for evaluation," is incorrect. Features are the input data used to make predictions, and labels are the output values the model is trying to predict. In machine learning, the model learns from the features to predict the labels. Therefore, it makes no sense to use features for training and labels for evaluation.

Option C, "Use labels for training and features for evaluation," is also incorrect. The model's goal is to learn how to predict the labels from the features. Therefore, it makes no sense to use labels for training.

Option D, "Randomly split the data into columns for training and columns for evaluation," is also incorrect. The model learns from the rows of data, not columns, so this type of split does not make sense.