Q. What is cross-validation? Mention the steps involved in cross-validation.
Ans: Cross-Validation refers to a technique in which we reserve a particular portion of a dataset on which we do not train the model. After the training is over, we test the resulting model on this portion of the data before finalizing it.
The steps involved in cross-validation are as follows –
1. Reserve a small portion of the data set called validation data.
2. Train the model using the remaining dataset
3. Test the model on the validation data set and check its accuracy.