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hi Iamp;#39;m Zack Dean mayor and Iamp;#39;m one of the co-authors of the carrot package I have a passion for data science and spend most of my time working on and thinking about problems in machine learning this course focuses on predictive rather than explanatory modeling we want models that do not over fit the training data and generalize well in other words our primary concern when modeling is do the models perform well on new data the best way to answer this question is to test the models on new data this simulates real-world experience in which you fit on one data set and then predict on new data where you do not actually know the outcome simulating this experience with a trained test split helps you make an honest assessment of yourself as a modeler this is one of the key insights of machine learning error metrics should be computed on new data because in sample validation or predicting on your training data essentially guarantees overfitting out-of-sample validation helps you
