The sklearn.metrics module's xe "sklearn.metrics module:classification_report function"xe "classification_report function from the sklearn.metrics module"classification_report function produces a table of classification metrics based on the expected and predicted values, as in: from sklearn.metrics import classification_report
Names = [str(digit) for digit in digits.target_names]
Print(classification_report(expected, predicted,

A) The precision column shows the total number of correct predictions for a given digit divided by the total number of predictions for that digit. You can confirm the precision by looking at each column in the confusion matrix.
B) The recall column is the total number of correct predictions for a given digit divided by the total number of samples that should have been predicted as that digit. You can confirm the recall by looking at each row in the confusion matrix.
C) The f1-score column is the average of the precision. The recall and the support column is the number of samples with a given expected value-for example, 50 samples were labeled as 4s, and 38 samples were labeled as 5s.
D) All of the above are true.

Question

The sklearn.metrics module's xe "sklearn.metrics module:classification_report function"xe "classification_report function from the sklearn.metrics module"classification_report function produces a table of classification metrics based on the expected and predicted values, as in: from sklearn.metrics import classification_report
Names = [str(digit) for digit in digits.target_names]
Print(classification_report(expected, predicted,

A) The precision column shows the total number of correct predictions for a given digit divided by the total number of predictions for that digit. You can confirm the precision by looking at each column in the confusion matrix. 
B) The recall column is the total number of correct predictions for a given digit divided by the total number of samples that should have been predicted as that digit. You can confirm the recall by looking at each row in the confusion matrix. 
C) The f1-score column is the average of the precision. The recall and the support column is the number of samples with a given expected value-for example, 50 samples were labeled as 4s, and 38 samples were labeled as 5s. 
D) All of the above are true.

Quizplus · Accepted Answer

All of the statements A, B, and C are true regarding the classification_report function from the sklearn.metrics module. The precision, recall, and f1-score are all calculated based on the expected and predicted values, and can be confirmed by looking at each column or row in the confusion matrix. The support column indicates the number of samples with a given expected value.

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