APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNINGHARD QUESTIONS
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Pick any of the 10 models you built for your model; use its error estimate on the held-out data
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Train a new model on the full data set, using the parameter you found; use the average CV error as its error estimate
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Average all of the 10 models you got; use the average CV error as its error estimate
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Average all of the 10 models you got; use the error the combined model gives on the full training set The best way to pick a final model is to train a new machine learning model on the full data set using the parameter learnt and to use the average cross-validation error as its error estimate.
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Detailed explanation-1: -Similarly, 10-fold=100/10=10. So, 90% of data will be used for training and 10% will be used for validation. You should divide your set to 5 parts, and in each part 80 % for training and 20% for testing. But in each subset different 20 % is for testing.
Detailed explanation-2: -model selection module’s cross val score helper class can be used for applying K-fold cross-validation in a simple manner. Use the LOOCV method for very small data sets. For very large data sets, one can use the value of K as 5. The value of K = 10 is standard value of K.
Detailed explanation-3: -Pick k – number of times the model will be trained. Pick a number of samples which will be the test set. Split the dataset. Train on the training set. Validate on the test set. Save the result of the validation. Repeat steps 3-6 k times. More items