Model evaluation is a critical process in the development of machine learning models and involves measuring and comparing the performance of models to determine their accuracy and effectiveness. The goal of model evaluation is to determine whether a model is capable of making accurate and consistent predictions on new data.
In the model evaluation process, a test data set is used to test the model and measure its performance in terms of specific metrics, such as accuracy, sensitivity, specificity, F1-score, and others. These metrics allow us to determine how well the model performs on the task for which it has been trained.
In addition to performance metrics, cross-validation techniques can also be used to assess the model's ability to generalise to new data. This is done by dividing the dataset into several training and test subsets and evaluating the model on each subset to determine its ability to make accurate predictions on unseen data.
The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
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