Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?(1 Point)AUC-ROCAccuracyLoglossMean-Squared-Error

The correct evaluation metric for a model with a continuous output variable is Mean-Squared-Error.

Here's why:

1. AUC-ROC: This is used for binary classification problems. It tells us how much model is capable of distinguishing between classes.

2. Accuracy: This is also used for classification problems. It is the ratio of number of correct predictions to the total number of input samples.

3. Logloss: Logarithmic loss (logloss) measures the performance of a classification model where the prediction input is a probability value between 0 and 1.

4. Mean-Squared-Error: This is used for regression problems (continuous output variable). It measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.

So, the correct answer is Mean-Squared-Error.