Latest News

Thursday, August 27, 2020

What Is Confusion Matrix

 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:


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


What fraction of positive predictions are correct?

False Positive Rate is also known as Specificity.

Calculation of Evaluation Metrics using Scikit learn:

Import accuracy_score,precision_score,recall_score,f1_score from sklearn.metrics module as given in the screen shot.

  • Google+
  • Pinterest

No comments

Post a Comment