docs: Updated readme with ensembleclassifier

README.md

@@ -43,14 +43,21 @@ X, y = make_classification(
 clf = dcpy.DistanceMetricClassifier()
 clf.fit(X, y)
 print(clf.predict([[0, 0, 0, 0]], metric="canberra"))
+
+# Example usage of EnsembleDistanceClassifier
+ensemble_clf = dcpy.EnsembleDistanceClassifier(feat_idx=0)
+ensemble_clf.fit(X, y)
+print(ensemble_clf.predict(X))
 ```
 
 ## Features
 - **Distance Metric-Based Classification**: Utilizes a variety of distance metrics for classification.
 - **Customizable for Scientific Goals**: Allows fine-tuning based on scientific objectives by selecting appropriate distance metrics and features, enhancing both computational efficiency and model performance.
 - **Interpretable Results**: Offers improved interpretability of classification outcomes by directly using distance metrics and feature importance, making it ideal for scientific applications.
-- **Efficient and Scalable**: Demonstrates lower computational requirements compared to traditional methods like Random Forests, making it suitable for large datasets
-- **Open Source and Accessible**: Available as an open-source Python package on PyPI, encouraging broad application in astronomy and beyond
+- **Efficient and Scalable**: Demonstrates lower computational requirements compared to traditional methods like Random Forests, making it suitable for large datasets.
+- **Open Source and Accessible**: Available as an open-source Python package on PyPI, encouraging broad application in astronomy and beyond.
+- **(NEW) Ensemble Distance Classification**: Leverages an ensemble approach to use different distance metrics for each quantile, improving classification performance across diverse data distributions.
+- **(NEW) Expanded Distance Metrics**: DistClassiPy now offers 43 built-in distance metrics, an increase from the previous 18. Additionally, users can still define and use custom distance metrics as needed.
 
 ## Documentation