Data Splitting: Training Set and Test Set in Machine Learning
Data Splitting: Training Set and Test Set in Machine Learning Data Splitting is one of the most important steps in Machine Learning and Artificial Intelligence. It involves dividing a dataset into separate parts for training and evaluating a machine learning model. The primary goal of data splitting is to ensure that the model performs well on new and unseen data. Training Set A Training Set is used to train the machine learning model. The model learns patterns, trends, and relationships from this data. For example, in a dataset of 1000 records, if 80% is used for training, the model learns from 800 records. Test Set A Test Set is used after training to evaluate the model's performance. The model has not seen this data before, making it an effective way to measure real-world accuracy. Common Data Splitting Ratios 70% Training and 30% Testing 80% Training and 20% Testing 90% Training and 10% Testing Among these, the 80:20 ratio is commonly used. Benefits of Data...