COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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None of these
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Cross Validation checks the algorithm if it is giving correct output or it crosses it wrong.
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Cross Validation does validation for different data by cross checking them.
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Cross-Validation is a model validation technique that splits the training dataset in a given number of “folds".
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Detailed explanation-1: -Cross validation is a technique used in machine learning to evaluate the performance of a model on unseen data. It involves dividing the available data into multiple folds or subsets, using one of these folds as a validation set, and training the model on the remaining folds.
Detailed explanation-2: -Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern.
Detailed explanation-3: -Cross-Validation is a resampling technique with the fundamental idea of splitting the dataset into 2 parts-training data and test data. Train data is used to train the model and the unseen test data is used for prediction.
Detailed explanation-4: -Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.
Detailed explanation-5: -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.