Data Splitting Intuition

Lecture 23:- Data Splitting Intuition

Data splitting is a crucial step in data analysis, machine learning, and model evaluation. The intuition behind data splitting is to divide the available dataset into separate subsets for different purposes, such as model training, validation, and testing. The main reasons for data splitting are:

Model Training: This is the first and most critical purpose of data splitting. The training set is used to train the machine learning model. The model learns patterns and relationships between features and labels in the training data, allowing it to make predictions on new, unseen data.

Model Validation: After training the model, it needs to be evaluated to ensure it generalizes well to new, unseen data. The validation set is used to tune hyperparameters, assess the model's performance, and avoid overfitting (when the model performs well on training data but poorly on new data).

Model Testing: Finally, once the model is trained and validated, it needs to be tested on a separate dataset to obtain an unbiased estimate of its performance. The testing set is used to evaluate the model's accuracy, precision, recall, and other performance metrics.

The process of data splitting involves dividing the original dataset into these three subsets: training set, validation set, and testing set. The division is typically performed randomly, but it's essential to ensure that the data in each subset is representative of the overall dataset to avoid any bias.

A common approach to data splitting is the 80-20 or 70-30 split, where the dataset is divided into 80% (or 70%) for training and 20% (or 30%) for testing. The training set can then be further split into training and validation subsets, using techniques like k-fold cross-validation or stratified sampling.

For example, let's say you have 1,000 data samples. You might split the data as follows:

• Training set: 800 samples (used to train the model)
• Validation set: 100 samples (used to tune hyperparameters and evaluate the model's performance)
• Testing set: 100 samples (used to obtain an unbiased estimate of the model's performance)

Data splitting is a fundamental practice to ensure that the machine learning model is robust, performs well on new data, and can be reliably deployed in real-world applications.