What is the classification report in machine learning?
We can use the classification_report() function to calculate the classification report of a classification problem using the sklearn library in Python. The classification report outputs the precision, recall, f1-score, and support for a classification problem.
Now, we know that:
True Positives (TP): True positives are those output labels that are predicted to be True, and they are actually True.
True Negatives (TN): True negatives are the output labels that are predicted to be False, and they are actually False also.
False Positives (FP): False positives are the output labels that are predicted to be True, but they are actually False.
False Negatives (FN): False negatives are those output labels that are predicted to be False, but they are actually True.
Precision in machine learning is defined as:
And recall in machine learning is defined as:
F1-score in machine learning is the harmonic mean of precision and recall. So, the F1 score is defined as:
And, the support in the classification report is the number of occurrences of each label in the true values of y.
Please also note that the accuracy in machine learning is defined as:
How to calculate the classification report using sklearn in Python?
We can use the following Python code to calculate the classification report for a classification problem using sklearn in Python…
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