Exam AI-900 topic 1 question 3 discussion

Answer 2 is also correct please refer the https://docs.microsoft.com/en-us/azure/machine-learning/classic/evaluate-model-performance

Answer 2 is correct. The grid used in the question is reversed to the MS documentation. A false negative would imply that a 0 was predicted but 1 was the actual outcome, of which there were 1033 occurrences according to the grid used in the question.

The confusion matrix for the model scored on test data reveals the following: - **True Positives (Correctly Predicted Positives)**: 11 - **False Negatives**: 5 This information is crucial for evaluating the model's performance, especially in a classification task. The true positives represent instances where the model correctly predicted the positive class, while false negatives indicate instances where the model failed to predict the positive class when it should have. Understanding these metrics helps assess the model's accuracy and effectiveness in identifying positive cases. Source: Conversation with Copilot, 27/6/2024

No matter the axis labels (X and Y), the positions of TP, FP, FN, and TN remain consistent within the matrix. Actual Predicted 1 0 1 11 5 0 1033 13951 Interpretation: True Positives (TP): Predicted 1, Actual 1 — Value: 11 False Positives (FP): Predicted 1, Actual 0 — Value: 5 False Negatives (FN): Predicted 0, Actual 1 — Value: 1033 True Negatives (TN): Predicted 0, Actual 0 — Value: 13951

In Question, Actual is on X-axis and Predicted is on 'Y' axis So answer will be 11 and 1033 TP = 11 TN = 13951 FP = 5 FN = 1033 In Solution, Actual Values are on 'Y-axis' and Predicted Values on 'X-axis' - Thus table get changed.

Hello team, I do not understand this can someone explain why the answer is 11:1033? I do not understand the logic. Thank you

I had a lot of difficulties with the answer for this question. However, now that I have been able to step back and look at the chart objectively, you realize that it is not a chart for determining, it a representational chart, where you are being presented the results. That is why the response is actually correct.

Explanation chart shown is wrong Should be TP FP FN TN

11 corresponds to 0-0 1033 corresponds to 0-1

Do not agree with the response for answer 2. If we have a binary classification 'let me say person is 'tall' or 'not tall' (two classes). The confusion matrix shows the top row as row 1 / column 1 which are the true positives (correctly identified as tall). The next value in top row: row 1 and column 0 that is defined as tall (row) and 0 (contains incorrectly classified as 'not tall') and is called false negative. I read the questions that 'false negative' is what they are asking for. table: tall: correctly predicted - not tall predicted not_tall: incorrectly classified as not tall - correctly identified as not tall.

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1. 11 2. 1033 https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2#confusion-matrix

answers and explanations are correct

PP = 11 and FN = 1033

The Microsoft table is transposed so use rationale to think through. Answers is correct.

they have transposed the matrix, if you pay close attention. Therefore, given answer is correct

11 / 1033