Which of the following is a method for evaluating the performance of a multi-class classification model?Review LaterConfusion matrixMean squared errorROC curveR-squared
Question
Which of the following is a method for evaluating the performance of a multi-class classification model?Review LaterConfusion matrixMean squared errorROC curveR-squared
Solution
The method for evaluating the performance of a multi-class classification model from the options provided is the Confusion Matrix.
Here's why:
-
Confusion Matrix: This is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. It includes information about the actual and the predicted classifications done by the machine learning model. It's a good method for multi-class classification model evaluation.
-
Mean Squared Error: This is more commonly used in regression analysis to see how close estimates or forecasts are to actual values
Similar Questions
Which method is used to evaluate the performance of a classification model using a held-out dataset?Precision-Recall CurveCross-ValidationROC CurveConfusion Matrix
In the context of multiclass classification, which evaluation metric takes into account both the false positive and false negative errors for each class, providing a more comprehensive view of model performance?Review LaterMacro F1-ScoreAccuracyHamming LossMicro F1-Score
Which of the following is a method for evaluating the performance of a binary classification model?Review LaterAccuracymean squared errorf1 scorer-sqaured
2. You need to evaluate a classification model. Which metric can you use? Mean squared error (MSE)PrecisionSilhouette
Classification models accuracy is measured byallmean square erroraccuracy scoreroot mean square error
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.