Topic 1 Question 19
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?
Receiver operating characteristic (ROC) curve
Misclassification rate
Root Mean Square Error (RMSE)
L1 norm
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Ans. A is correct
👍 19rsimham2021/09/26Answer is A. An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds
👍 7AKT2021/10/01A is indeed correct see https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: • True Positive Rate • False Positive Rate True Positive Rate (TPR) is a synonym for recall and is therefore defined as follows: TPR = TP/TP+FN False Positive Rate (FPR) is defined as follows: FPR = FP/FP+TN
👍 3GeeBeeEl2021/10/12
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