When training a model, why should you randomly split the rows into separate subsets?
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A. B. C.C
The correct answer is C. to test the model by using data that was not used to train the model.
When training a machine learning model, it is important to have a way to evaluate how well the model will perform on new, unseen data. One way to do this is to split the available data into two sets: a training set, used to train the model, and a test set, used to evaluate the model's performance.
The data should be randomly split into these two subsets, to ensure that the model is not biased towards any particular subset of the data. If the data were split sequentially, for example, with the first 80% used for training and the remaining 20% used for testing, the model might learn to perform well on the specific subset of the data used for training, but not generalize well to new, unseen data.
By randomly splitting the data into training and test sets, we can ensure that the model learns to recognize patterns that are generalizable to new data, and not just specific to the training set.
Additionally, it is common to split the data into three subsets: a training set, a validation set, and a test set. The validation set is used to evaluate the model's performance during training and to help with model selection (e.g. choosing hyperparameters or selecting between different model architectures). The test set is used only at the very end of the process, to evaluate the final performance of the selected model.
Overall, randomly splitting the data into separate subsets is an important step in machine learning model training, as it allows us to evaluate the model's performance on new, unseen data and avoid overfitting to the training set.