Confusion matrix.txt

A confusion matrix is a technique for summarizing the
performance of a classification algorithm.

Classification accuracy alone can be misleading if you have 
an unequal number of observations in each class or if you have 
more than two classes in your dataset.

Calculating a confusion matrix can give you a better idea of 
what your classification model is getting right and what types of 
errors it is making.


A confusion matrix is a summary of prediction results on a 
classification problem.

The number of correct and incorrect predictions are summarized 
with count values and broken down by each class. This is the key 
to the confusion matrix.

The confusion matrix shows the ways in which your classification model
is confused when it makes predictions.

It gives you insight not only into the errors being made by your classifier 
but more importantly the types of errors that are being made.

It is this breakdown that overcomes the limitation of using 
classification accuracy alone.


1. True Positives - The values that
were actually True and were
predicted to be True as well.
2. True Negatives - The values that
were actually False and were
predicted to be False as well.



If all the data predicted by our
algorithm lies in these two buckets,
our ML algorithm is 100% accurate.
However, that is rarely the case.
There are two other buckets in which
our predictions can lie:


3. False Positives - The values that
were actually False but were predicted to be True.
4. False Negatives - The values that
were actually True but were predicted
to be False.

The accuracy of a model is
equal to:
(True Positives + True Negatives) /
(True Positives + True Negatives +
False Positives + False Negatives)