Update Readme

README.md

@@ -7,19 +7,13 @@ sdk: gradio
 sdk_version: 3.17.0
 app_file: app.py
 pinned: false
----
 tags:
 - evaluate
 - metric
 
 description: >-
-  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
-  Accuracy = (TP + TN) / (TP + TN + FP + FN)
-  Where:
-  TP: True positive
-  TN: True negative
-  FP: False positive
-  FN: False negative
+ Compute confusion matrix to evaluate the accuracy of a classification. By definition a confusion matrix :math:C is such that :math:C_{i, j} is equal to the number of observations known to be in group :math:i and predicted to be in group :math:j.
+ Thus in binary classification, the count of true negatives is :math:C_{0,0}, false negatives is :math:C_{1,0}, true positives is :math:C_{1,1} and false positives is :math:C_{0,1}.
 ---
 
 # Metric Card for Confusion Matrix