Evaluating Model Accuracy

We need to assess the accuracy of an ML model to evaluate how well it performs on the task it was designed to solve. Accurate models are more useful in practical applications because they can produce reliable predictions or classifications. Assessing the accuracy of a ML model can also help to identify areas for improvement in the model or the data used to train it.

Moreover, assessing the accuracy of a ML model is important because it helps to avoid overfitting. Overfitting occurs when a model performs well on the training data but poorly on new data. Assessing accuracy on test data can help to detect overfitting and prevent it by adjusting the model’s complexity or regularization.

In addition, accuracy assessment is also important for comparing different ML models or algorithms on the same problem. It helps to determine which model or algorithm performs better and is more suitable for the specific application.