Free Preparation Discussions
MENU
Snowflake DSA-C02 Exam - Topic 1 Question 5 Discussion
Topic #: 1
Which metric is not used for evaluating classification models?
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.
Sarina
9 months agoBette
9 months agoElinore
9 months agoIluminada
10 months agoJaclyn
10 months agoSamuel
10 months agoJoseph
10 months agoNida
11 months agoElenor
11 months agoMeaghan
11 months agoMartina
11 months agoGoldie
11 months agoEvangelina
11 months agoYoulanda
11 months agoBrett
11 months agoShaun
11 months agoRonnie
11 months ago