Data Splitting

Lecture 24:- Data Splitting

Data splitting is a crucial step in machine learning and data analysis workflows. It involves dividing a dataset into separate subsets for training, validation, and testing purposes. The main purpose of data splitting is to assess the performance of a machine learning model accurately and avoid overfitting.

Here's how data splitting is typically done:

Training Set: The training set is the largest subset of the dataset, typically accounting for 60-80% of the total data. It is used to train the machine learning model by feeding the input features and corresponding labels to the model. During training, the model learns from the data and adjusts its internal parameters to make accurate predictions.

Validation Set: The validation set is a smaller subset of the dataset, usually around 10-20% of the total data. It is used to tune hyperparameters of the machine learning model and assess its performance during training. Hyperparameters are parameters that are set before training, and they significantly impact the model's behavior. By evaluating the model on the validation set, you can choose the best hyperparameters that yield the best performance.

Testing Set: The testing set is another separate subset of the dataset, typically around 10-20% of the total data. It is used to evaluate the model's performance after it has been trained and tuned using the training and validation sets. The testing set serves as an independent dataset that the model has never seen before, allowing you to get an unbiased estimate of its performance on new, unseen data.

The process of data splitting is typically done randomly, ensuring that each subset is representative of the overall dataset's distribution. Randomness helps avoid bias and ensures that the model generalizes well to new data.

In some cases, especially with limited data, k-fold cross-validation is used instead of a single validation set. In k-fold cross-validation, the dataset is divided into k equally sized folds, and the model is trained and validated k times. Each time, a different fold serves as the validation set, and the remaining folds are used for training. This technique provides a more robust estimate of the model's performance and helps mitigate the impact of random variations in the data splitting process.

Data splitting is essential to accurately assess a machine learning model's performance, avoid overfitting, and ensure the model's ability to generalize well to new data. It is a critical step in the model development and evaluation process.