Update README.md

Author: ajitsingh98

README.md

@@ -1,4 +1,4 @@
-# All-About-Performance-Metrics
+# Evaluation Metrics in Machine Learning 
 This repository contains a comprehensive collection of performance metrics for various machine learning tasks, including regression, classification, and clustering. These metrics have been implemented from scratch to provide a reliable and customizable way of evaluating the performance of your machine learning models.
 
 ## Table of Contents
@@ -25,6 +25,9 @@ The repository currently includes the following performance metrics:
 - Mean Absolute Error (MAE)
 - R-squared (R2) Score
 - Adjusted R-squared (R2) Score
+- Pearson correlation
+- spearman correlation
+
 
 ### [Classification Metrics](classification_metrics.ipynb)
 
@@ -35,7 +38,9 @@ The repository currently includes the following performance metrics:
 - Recall Score
 - Log Loss/ Binary Cross Entropy Loss
 - Area Under the Receiver Operating Characteristic Curve (ROC AUC)
--  Classification report
+- Classification report
+- Average precision
+- precision-recall curve
 
 ### [Clustering Metrics](clustering_metrics.ipynb)
 
@@ -91,4 +96,4 @@ This repository is licensed under the MIT License. See the [LICENSE](LICENSE) fi
 
 ---
 
-I hope that this repository and the included performance metrics will be valuable tools in evaluating the effectiveness of your machine learning models. Feel free to explore, experiment, and contribute to further improve the available metrics. If you have any questions or encounter any issues, please don't hesitate to reach out to me. Happy modeling and evaluating!
+I hope that this repository and the included performance metrics will be valuable tools in evaluating the effectiveness of your machine learning models. Feel free to explore, experiment, and contribute to further improve the available metrics. If you have any questions or encounter any issues, please don't hesitate to reach out to me. Happy modeling and evaluating!