The selected features were assessed across five diverse ML classifiers: Logistic Regression, Support Vector Machines, Naïve Bayes, Random Forest and XGBoost. Within the training dataset, a fivefold cross-validation using stratified k-fold was performed for each of the five classifiers.

Stratified K-Fold Cross-Validation preserves the class distribution within each fold to ensure consistent representation of different classes.Review LaterTrueFalse

A data analyst trained a machine learning model to classify whether a transaction is fraudulent or not. The model had a training score of 89% and a test score of 92% percent. It was suggested that the high score on the test set may be due to the fact that the test split was easy to predict on. The analyst was then advised to use cross validation so that he gets metrics on different and random splits. Which of the following is/are the possible algorithms that can be used?- i. KFold cross validation- ii. Shuffle Split Cross validation- iii. Grid search cross validation- iv. Linear regressioni. onlyi. and ii onlyiii. and iv onlyiii. onlyiv. onlyii. only

. Rerun the classification using any combination of features and classifiers.

If we only have a small number of observations, K-fold cross validation provides a better estimate of the generalization error than the validation set method.Group of answer choicesTrueFalse

Choose the most widely used mattress and tools to assess the classification models.(1 Point)The area under the ROC curveConfusion matrixCost-sensitive accuracyAll of the above