How is the Test Dataset Used?
- Training: After the training process, the model is applied to the test dataset.
- Evaluation: The model’s predictions are compared to the actual, known values in the test data.
- Metrics: Performance is measured using evaluation metrics (accuracy, precision, recall, etc.) to gauge the model’s effectiveness.
- Iteration: If results are unsatisfactory, the model may be refined or retrained with adjustments based on the test data insights.
What is Test Dataset in Machine Learning?
In Machine Learning, a Test Dataset plays a crucial role in evaluating the performance of your trained model. In this blog, we will delve into the intricacies of test dataset in machine learning, its significance, and its indispensable role in the data science lifecycle.