APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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To assess the predictive performance of the models
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To judge how the trained model performs outside the sample on test data
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Both A and B
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none of the above
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Detailed explanation-1: -What is the purpose of performing cross-validation? C. Cross-validation is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.
Detailed explanation-2: -Based on the models performance on unseen data we can say weather our model is Under-fitting/Over-fitting/Well generalized. Cross validation (CV) is one of the technique used to test the effectiveness of a machine learning models, it is also a re-sampling procedure used to evaluate a model if we have a limited data.
Detailed explanation-3: -Cross-Validation dataset: It is used to overcome the disadvantage of train/test split by splitting the dataset into groups of train/test splits, and averaging the result. It can be used if we want to optimize our model that has been trained on the training dataset for the best performance.