Which of the following statements is NOT true about the Decision tree?

What is the advantage of AUC (Area Under the ROC Curve) compared to metrics like accuracy, precision, recall, or F1 score?

The graph below shows training and validation loss across epochs, with the early stopping point marked. Which of the following is not a benefit of using early stopping?

You trained a model that performs very well on the training data but poorly on the testing data. Which of the following actions is most likely to help with this issue?

Which of the following choices best explains the relationship illustrated by these graphs?  

Which of the following statements is true about k-NN?

Which of the following correctly describes common regression evaluation metrics? Note: yᵢ = actual value ŷᵢ (y-hat) = predicted value ȳ (y-bar) = mean of actual values m: number of samples Formulas: Mean Absolute Error (MAE) = (1/m) × Σ|yᵢ − ŷᵢ| Mean Squared Error (MSE) = (1/2m) × Σ(yᵢ − ŷᵢ)² Root Mean Squared Error (RMSE) = √MSE Coefficient of Determination (R²) = 1 − [Σ(yᵢ − ŷᵢ)² / Σ(yᵢ − ȳ)²]

The graph below shows two curves fitted to the same dataset using high-degree polynomial models. Which of the following best explains the effect of regularization in this context?  

What role does the sigmoid function play in logistic regression?

In a decision tree, splitting criteria are typically based on: