Data Partitioning $($from section $11\mathrm{.}3$ Performance Evaluation$)$

Break the data down into two data sets, the TRAINING data and TESTING data.

Why is it important to have data for testing that is different from the training data?

$***$ SELECT ALL CORRECT ANSWERS $***$

Performance Evaluation: Testing data allows us to measure the performance of the predictive model objectively. By comparing the model's predictions with the actual outcomes in the testing data, we can calculate various performance metrics such as accuracy, precision, recall, or mean squared error. These metrics provide insights into how well the model is performing and help assess its usefulness and reliability.

Decision Making: In many real$-$world applications, the ultimate purpose of predictive analytics is to make informed decisions based on the model's predictions. Testing data allows us to estimate how well the model will perform in practical scenarios. By evaluating the model's performance on testing data, we can gain confidence in its ability to assist in decision making.

Avoiding Overfitting: Overfitting occurs when a model becomes overly specialized in the training data and fails to generalize well to new data. If the same data used for training is also used for testing, the model may simply memorize the training examples without truly understanding the underlying patterns. Having separate testing data helps identify if the model is overfitting, as it evaluates the model's performance on unseen examples.