Difference Between Training and Testing Data
In machine learning, data is divided into two main parts: training data and testing data. These are used at different stages of model development and serve different purposes.
Training data is the data used to train a machine learning model. It includes input features along with their correct output labels. The model learns from this data by finding patterns and relationships between input and output. It is during this phase that the model adjusts its internal parameters to minimize errors.
Testing data, on the other hand, is a separate portion of the dataset that is not used during the training phase. It is used to evaluate how well the trained model performs on new, unseen data. Testing helps to check whether the model has learned correctly or is just memorizing the training data (overfitting).
The training dataset is usually larger in size compared to the testing dataset because more data is needed to learn patterns effectively. Testing data is smaller but crucial to measure the model’s accuracy and generalization ability.
In conclusion, training data helps in building the model, while testing data helps in evaluating its performance on real-world or unseen data.
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