## What is a Confusion Matrix?

Confusion Matrix is a technique to summarize the
performance of an algorithm typically a supervised learning one.Most of
the time we use confusion Matrix to visualize the performance of Classification
algorithm.

Each row of the matrix represents the instances in a
predicted class while each column represents the instances in an actual class
(or vice versa).

It is a special kind of contingency table, with two
dimensions ("actual" and "predicted"), and identical sets
of "classes" in both dimensions (each combination of dimension and
class is a variable in the contingency table)

### Four Basic Evaluation Metrics of confusion Matrix:

#### Accuracy:

For what fraction of all instances is the classifier;s
prediction correct(for either positive or negative c;ass)?

#### Classification Error:

For what fraction of all instance is the
classification incorrect?

#### Recall (True positive Rate):

What fraction of all positive instance does the
classifier correctly identify as positive?

#### Recall is also known as

- True Positive Rate
- Sensitivity
- Probability of Detection

#### Precision:

What fraction of positive predictions are correct?

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