Binary Classification Model Performance Analysis

Answer: C.

Option A is incorrect because based on the confusion matrix, the model tends to classify cases “not at risk” when actually they are, hence the model is poor at predicting one of the classes.

Option B is incorrect because based on the confusion matrix, the model tends to classify cases “at risk” when they are actually not, hence the model is poor at predicting one of the classes.

Option C is CORRECT because, based on the confusion matrix, a classification model's performance is good if the number of true positives (TP) is higher than that of the false positives (FP), and the same is true for the true negatives (TN) vs.

false negatives (FN).

Option D is incorrect because it actually can be used.

The confusion matrix visualizes the frequency of the correctly and incorrectly classified cases vs.

the frequency of actual cases in each category.

You can use it to draw initial conclusions about the model's performance.

Other metrics like precision, recall should be used, as well.

Diagram - Confusion matrix example:

Reference:

The confusion matrix is a table that summarizes the performance of a classification model by comparing its predictions against the actual values of the target variable. It contains four elements: True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN). The matrix is commonly used to evaluate the accuracy of a machine learning model and determine how well it performs at correctly predicting positive and negative instances.

In this scenario, the binary classification model has been trained to predict if a machinery is at risk of breaking down. The real-time vibration data coming from turbines of a power plant are used as inputs to the model to make predictions in real-time. The Run object's log_confusion_matrix() method has been used to log the confusion matrix results of the runs.

The confusion matrix provides information on the performance of the model by showing the relationship between the predicted labels and the actual labels. The four elements of the matrix are:

• True Positives (TP): the number of instances that are positive and were correctly predicted as positive.
• False Positives (FP): the number of instances that are negative but were incorrectly predicted as positive.
• True Negatives (TN): the number of instances that are negative and were correctly predicted as negative.
• False Negatives (FN): the number of instances that are positive but were incorrectly predicted as negative.

To determine if the model is performing well, we need to examine the values of TP, FP, TN, and FN in the confusion matrix.

Option A states that TP is high, FP is low, TN is low, and FN is high. This scenario means that the model is correctly identifying many instances of positive cases (TP is high), but it is also incorrectly predicting a significant number of negative cases as positive (FP is low, but TN is low). Additionally, there are many instances of actual positive cases that are being predicted as negative (FN is high), which is not ideal for a predictive maintenance solution. Therefore, this relationship does not support the conclusion that the model is performing well.

Option B states that TP is low, FP is high, TN is high, and FN is low. This relationship implies that the model is correctly predicting negative cases (TN is high), but it is also incorrectly predicting many positive cases as negative (FP is high), which is not ideal for a predictive maintenance solution. Additionally, there are few instances of actual positive cases that are being predicted as negative (FN is low), which is good, but it is not enough to conclude that the model is performing well.

Option C states that TP is high, FP is low, TN is high, and FN is low. This scenario means that the model is correctly identifying many instances of positive cases (TP is high) and is also correctly predicting negative cases (TN is high). Additionally, there are few instances of actual positive cases that are being predicted as negative (FN is low), which is good for a predictive maintenance solution. Therefore, this relationship supports the conclusion that the model is performing well.

Option D states that the confusion matrix cannot be used to support the conclusion. This statement is incorrect because the confusion matrix is a fundamental tool for evaluating the performance of classification models.

In summary, the relationship that supports the conclusion that the model is performing well is Option C, where TP is high, FP is low, TN is high, and FN is low.