Splitter

See also: Machine learning terms

Splitter in Machine Learning

A splitter in the context of machine learning refers to a method or technique used to divide a dataset into subsets, typically for the purposes of training, validation, and testing. The process of splitting data helps to prevent overfitting, generalizes the model, and provides a more accurate evaluation of a model's performance. Various techniques exist for splitting data, such as k-fold cross-validation, stratified sampling, and time series splitting.

K-Fold Cross-Validation

K-fold cross-validation is a widely used splitting technique, particularly for evaluating the performance of a machine learning model. In this method, the dataset is partitioned into k equally sized folds. The model is then trained on k-1 folds and validated on the remaining fold. This process is repeated k times, with each fold being used as a validation set exactly once. The final model performance is evaluated using the average of the performance metrics obtained from all k iterations.

Stratified Sampling

Stratified sampling is a method used to ensure that each subset of data contains a proportionate representation of the different classes or categories present in the original dataset. This technique is particularly useful in cases where the dataset is imbalanced or when the distribution of classes is crucial for model performance. By maintaining the same proportion of classes in both the training and validation datasets, stratified sampling ensures that the model can generalize better across all classes.

Time Series Splitting

Time series data is characterized by an inherent temporal ordering, making it essential to preserve this order when splitting the dataset. Time series splitting involves dividing the dataset into contiguous segments in chronological order, with the first segment used for training and subsequent segments used for validation and testing. This technique ensures that the model is not exposed to future data during the training process, thus preserving the temporal structure and avoiding data leakage.

Explain Like I'm 5 (ELI5)

Imagine you want to teach your little robot to recognize different animals. You have lots of pictures of animals to help your robot learn. To make sure your robot really knows its stuff, you need to test it using some of the pictures.

A splitter in machine learning is like a helper who organizes the pictures into groups for teaching and testing the robot. The helper makes sure that the robot sees a variety of animals in each group, so it learns how to recognize all of them properly. Once the robot has seen many different groups of pictures, you can be more confident that it can recognize animals it hasn't seen before.