Mastering Classification Metrics

What is precision in the context of classification?

• Precision is the ratio of true positives to the total number of predictions.

• Precision measures the overall accuracy of the classification model.

• Precision is the ratio of true positives to the sum of true positives and false positives.

• Precision is the number of true positives divided by the number of true negatives.

How is recall defined in classification metrics?

• Recall = True Positives / (True Positives + False Negatives)

• Recall = True Positives / Total Samples

• Recall = False Positives / (False Positives + True Negatives)

• Recall = True Negatives / (True Negatives + False Positives)

If a model has a precision of 0.8 and a recall of 0.6, what is the F1 score?

• 0.686

• 0.75

• 0.5

• 0.9

What does a confusion matrix represent in classification?

• A confusion matrix represents the performance of a classification model by showing the counts of true positives, true negatives, false positives, and false negatives.

• A confusion matrix is used to visualize the training data.

• A confusion matrix shows the overall accuracy of a model.

• A confusion matrix represents the number of features in a dataset.

In a confusion matrix, what do true positives (TP) indicate?

• True positives indicate the total number of instances in the dataset.

• True positives indicate correctly predicted positive instances.

• True positives indicate incorrectly predicted positive instances.

• True positives indicate correctly predicted negative instances.

How do you calculate classification accuracy?

• Classification accuracy = (Number of correct predictions / Total number of predictions) * 100

• Classification accuracy = (Total number of predictions / Number of correct predictions) * 100

• Classification accuracy = (Number of correct predictions + Number of incorrect predictions) / Total number of predictions

• Classification accuracy = (Number of incorrect predictions / Total number of predictions) * 100

What is the formula for precision?

• Precision = True Positives / (True Positives + False Positives)

• Precision = True Positives + False Positives

• Precision = True Positives / Total Samples

• Precision = True Negatives / (True Negatives + False Negatives)

What is the formula for recall?

• Recall = True Positives / Total Samples

• Recall = False Positives / (False Positives + True Negatives)

• Recall = True Negatives / (True Negatives + False Positives)

• Recall = True Positives / (True Positives + False Negatives)

If a model predicts 70 true positives, 10 false positives, and 20 false negatives, what is its precision?

• 0.75

• 0.875

• 0.9

• 0.6

What is the relationship between precision and recall?

• Precision and recall are two metrics used to evaluate the performance of a classification model, where precision focuses on the accuracy of positive predictions and recall focuses on the ability to identify all relevant instances.

• Precision is the same as recall.

• Precision is only relevant for negative predictions.

• Recall measures the total number of predictions made.