# Disambiguating The Confusion Matrix

2018-09-11

How many terms are possible? There are four basic ingredients: TP, FP, TN, and FN. So if each term may or may not be included in a sum in the numerator and denominator, that’s 16 possible numerators and 16 denominators, for a total of 256 possible terms to remember. Some of these are redundant, such as one(a.k.a. ONE), given by TP/TP, FP/FP, etc. If we insist that the numerator and denominator be different, that eliminates 16 possibilities, and we’re down to a more manageable 240 definitions. And if we rule out terms that are the reciprocals of other terms, we’re down to only 120 definitions to memorize.

And of those, John points out the handful which are generally important, providing us an excellent table with definitions of commonly-used terms.

## Interpreting The Area Under The Receiver Operating Characteristic Curve

2018-09-19

Roos Colman explains what a Receiver Operating Characteristic (ROC) curve is and how we interpret the Area Under the Curve (AUC): The AUC can be defined as “The probability that a randomly selected case will have a higher test result than a randomly selected control”. Let’s use this definition to calculate and visualize the estimated […]

## Naive Bayes Against Large Data Sets

2018-09-12

Catherine Bernadorne walks us through using Naive Bayes for sentiment analysis: The more data that is used to train the classifier, the more accurate it will become over time. So if we continue to train it with actual results in 2017, then what it predicts in 2018 will be more accurate. Also, when Bayes gives […]

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