To evaluate a binominal classification machine learning model, you examine this confusion matrix: What can you infer from this confusion matrix? (Select ALL correct answers) A. This model had 67 false positive predictions. B. This model had 67 false negative predictions. C. This model was able to correctly predict 705 "BAD" values out of a total of 772 "BAD" values in the ExampleSet. D. Data scientists would consider this a 'balanced' data set.
Question
To evaluate a binominal classification machine learning model, you examine this confusion matrix:
What can you infer from this confusion matrix? (Select ALL correct answers)
A. This model had 67 false positive predictions. B. This model had 67 false negative predictions. C. This model was able to correctly predict 705 "BAD" values out of a total of 772 "BAD" values in the ExampleSet. D. Data scientists would consider this a 'balanced' data set.
Solution
To evaluate the confusion matrix, let's break down the components and analyze the information provided:
A confusion matrix typically looks like this:
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | True Positive (TP) | False Negative (FN) |
| Actual Negative | False Positive (FP) | True Negative (TN) |
Given the options, let's infer the correct answers:
A. This model had 67 false positive predictions.
- False Positive (FP) refers to the cases where the model predicted "Positive" but the actual value was "Negative". If the confusion matrix shows 67 in the FP cell, this statement is correct.
B. This model had 67 false negative predictions.
- False Negative (FN) refers to the cases where the model predicted "Negative" but the actual value was "Positive". If the confusion matrix shows 67 in the FN cell, this statement is correct.
C. This model was able to correctly predict 705 "BAD" values out of a total of 772 "BAD" values in the ExampleSet.
- "BAD" values are typically considered the "Positive" class. True Positive (TP) refers to the cases where the model correctly predicted "Positive". If the confusion matrix shows 705 in the TP cell and the total number of actual "BAD" values (TP + FN) is 772, this statement is correct.
D. Data scientists would consider this a 'balanced' data set.
- A balanced data set means that the number of instances of each class (Positive and Negative) is roughly equal. To determine if the data set is balanced, we need to compare the total number of actual Positive and Negative instances. If these numbers are roughly equal, this statement is correct.
Without the actual confusion matrix values, we cannot definitively confirm the correctness of each statement. However, based on the typical structure of a confusion matrix, the analysis above provides a guideline for evaluating each option.
Similar Questions
Which evaluation metric is commonly used for binary classification problems and measures the proportion of true positive predictions among all positive examples?Select one:a. Recallb. Precision
Consider a classification problem with three classes: A, B, and C. A machine learning model is trained on a labeled dataset, and the confusion matrix for the model's predictions is given below:What is the overall accuracy of the model?a)0.69b)0.85c)0.8d)0.725
What is the purpose of a confusion matrix in machine learning?To visualize complex datasetsTo describe the distribution of the datasetTo evaluate the performance of a classification modelTo reduce overfitting in models
In evaluating the performance of a binary classifier (Positive and Negative), aconfusion matrix is used. State and explain the outcomes found in the matrix.
Explain the Confusion Matrix with Respect to Machine Learning Algorithms
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