Model evaluation is essential to assess prediction accuracy from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Model evaluation is essential to assess prediction accuracy. It allows us to estimate how well our model will generalize to new, unseen data. Without proper evaluation, we have no way of knowing whether our model is performing well or if it is just memorizing the training data. There are several methods for evaluating a model, each with its strengths and weaknesses. One common approach is to split the data into training and test sets. The model is trained on the training set and then evaluated on the test set. This gives us a good estimate of how well the model will perform on new data. However, this approach can lead to overfitting if the test set is used too many times for model evaluation.
Another method is cross-validation, where the data is split into k folds and the model is trained and evaluated k times. This gives us a more reliable estimate of how well the model will generalize to new data...
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