The Sklearn

question 65

Multiple Choice

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.

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Definitions:

Straight-Line Depreciation

An operation for dividing the expense of a solid asset through its functional duration in even yearly sums.

Simple Rate of Return

A calculation that estimates the profitability of an investment, dividing annual incremental net operating income by the initial investment cost.

Cash Operating Costs

Expenses a business incurs through its operations that require cash payments, excluding non-cash expenses like depreciation.

Automating Process

The method of using technology to perform tasks with minimal human intervention.