July 2019
Beginner to intermediate
740 pages
16h 52m
English
Content preview from Hands-On Data Analysis with Pandas
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Confusion matrix
As we discussed in Chapter 8, Rule-Based Anomaly Detection, a classification problem can be evaluated by comparing the predicted labels to the actual labels using a confusion matrix:
Each prediction can be one of four outcomes, based on how it matches up to the actual value:
- True Positive (TP): Correctly predicted to be the positive class
- False Positive (FP): Incorrectly predicted to be the positive class
- True Negative (TN): Correctly predicted to not be the positive class
- False Negative (FN): Incorrectly predicted to not be the positive class
False positives are also referred to as type I errors, while false negatives are ...
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