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?
False Positive Rate is also known as Specificity.
Calculation of Evaluation Metrics using Scikit learn:
No comments
Post a Comment