Machine Learning-2

Which metric is most appropriate for evaluating a binary classification model?

• Mean Squared Error (MSE)

• Accuracy, Precision, Recall, and F1-Score

• Root Mean Squared Error (RMSE)

• R-Squared Error

• Mean Absolute Error (MAE)

What does Precision measure in a classification model?

• The proportion of actual positives that are correctly identified

• The proportion of predicted positives that are actually correct

• The proportion of actual negatives that are correctly identified

• The overall accuracy of the model

How is the F1 Score calculated?

• (Precision + Recall) / 2

• 2*(Precision *Recall) / (Precision + Recall)

• Precision / Recall

• (Precision - Recall) / (Precision + Recall)

• (Precision + Recall) / (Precision -Recall)

How is Recall calculated?

• TP / (TP + FN)

• TN / (TN + FP)

• TP / (TP + FP)

• FN / (TP + FN)

• (TP+TN)/(TP+TN+FP+FN)

If Precision is 0.8 and Recall is 0.6, what is the F1 Score?

• 0.74

• 0.72

• 0.70

• 0.68

• 0.66

Which metric is more important when false positives are costly?

• Recall

• Accuracy

• Precision

• F1 Score

• False Positive Rate

Which of the following is true about Precision-Recall trade-off?

• Increasing Precision always increases Recall

• Increasing Recall always increases Precision

• Increasing Recall may decrease Precision

• Precision and Recall are independent of each other

If Precision is 1.0, what does it mean?

• The model predicted all positives and negatives correctly

• The model has a perfect Recall also

• The model predicted all negatives correctly

• The model predicted all positives correctly without any false positives

If a model has high Precision but low Recall, what does it indicate?

• The model is predicting many false positives

• The model is predicting many false negatives

• The model is predicting many True negatives

• The model is balanced

• The model is overfitting

A classifier predicts 100 positive cases, out of which 80 are correct. The total actual positive cases are 90. What is the Recall?

• 80.8%

• 88.8%

• 92.2%

• 76.7%

• 80%