Imagine you are working on a project which is a binary classification problem. You trained a model on training dataset and get the below confusion matrix on validation dataset. TN = 50, TP= 100, FP=10, FN= 5, which of the following prediction is correct? 1. Accuracy is ~0.91 2. Misclassification rate is ~ 0.91 3. False positive rate is ~0.95 4. True positive rate is ~0.95 Select one: a. 2 and 3 b. 1 and 4 c. 2 and 4 d. 1 and 3

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16-12-2022
Mathematics

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Imagine you are working on a project which is a binary classification problem. You trained a model on training dataset and get the below confusion matrix on validation dataset. TN = 50, TP= 100, FP=10, FN= 5, which of the following prediction is correct? 1. Accuracy is ~0.91 2. Misclassification rate is ~ 0.91 3. False positive rate is ~0.95 4. True positive rate is ~0.95 Select one: a. 2 and 3 b. 1 and 4 c. 2 and 4 d. 1 and 3

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debjitbhowal4568 debjitbhowal4568 · Answer 1 · 2022-12-21T06:10:31-05:00

The correct option is b - 1 and 4 which says that the accuracy is -0.91 and the true positive rate is -0.95

Data scientists who develop machine learning systems rely on confusion matrices to solve classification problems containing two or more classes. The matrix organizes input and output data in a way that allows analysts and programmers to visualize the accuracy, recall, and precision of the machine learning algorithms they apply to system designs.

In a two-class, or binary, classification problem, the confusion matrix is crucial for determining two outcomes. The outcomes can be positive or negative, where these variables represent numerical values in a machine learning system. When computing binary classification problems, you can use confusion matrices to find:

Accuracy rate: This is the percentage of times a classifier is correct.

Misclassification rate: This is the percentage of times a classifier is incorrect.

True positive rate: This figure represents the percentage of times a classifier correctly predicts desired outcomes.

False positive rate: This is a Type I error representing how often a classifier is incorrect when predicting desirable outcomes.

To know more about confusion matrices visit: brainly.com/question/11367104

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