The correct answer is False.
K-fold cross-validation is a resampling technique for evaluating machine learning models. It works by dividing the data into k equal-sized subsets, called folds. The model is then trained on k-1 folds and evaluated on the remaining fold. This process is repeated k times, with each fold used once as the validation set. The average performance of the model on the validation sets is then used to estimate the model’s performance on unseen data.
The value of k is a hyperparameter that must be chosen by the user. A larger value of k will result in a more accurate estimate of the model’s performance, but it will also take longer to train the model. A smaller value of k will result in a less accurate estimate of the model’s performance, but it will train the model faster.
The statement “For k cross-validation, larger k value implies more bias” is false. In fact, the opposite is true: a larger value of k will result in a lower bias. This is because k-fold cross-validation is a form of cross-validation, and cross-validation is a technique that is designed to reduce bias.
Cross-validation works by dividing the data into multiple subsets. The model is then trained on one subset and evaluated on the remaining subsets. This process is repeated multiple times, with each subset used once as the validation set. The average performance of the model on the validation sets is then used to estimate the model’s performance on unseen data.
Cross-validation is a more accurate way to estimate the model’s performance than simply training the model on the entire dataset and evaluating it on the same dataset. This is because cross-validation helps to reduce the effects of overfitting. Overfitting occurs when the model learns the training data too well and is not able to generalize to unseen data.
The more folds that are used in cross-validation, the more accurate the estimate of the model’s performance will be. However, using more folds will also take longer to train the model. Therefore, the value of k must be chosen carefully, balancing the need for accuracy with the need for speed.