Model evaluation is essential to assess the performance of algorithms from "summary" of Machine Learning by Ethem Alpaydin
Model evaluation is essential to assess the performance of algorithms. The main goal of machine learning is to develop models that can generalize well to new, unseen data. To achieve this goal, it is crucial to evaluate how well a model performs on a given dataset. Model evaluation helps us understand how well a model is able to make predictions and whether it is suitable for the task at hand. There are various metrics and techniques that can be used to evaluate the performance of machine learning models. One common approach is to split the dataset into training and testing sets, where the training set is used to train the model and the testing set is used to evaluate its performance. This allows us to assess how well the model generalizes to new ...
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