/Performance_Metrics

This repository contains custom implementations of various performance metrics for evaluating classification and regression models. The metrics are calculated and demonstrated on different datasets.

Primary LanguageJupyter Notebook

Performance Metrics Implementation: This repository contains custom implementations of various performance metrics for evaluating classification and regression models. The metrics are calculated and demonstrated on different datasets.

Contents: Classification Metrics Custom implementation of precision, recall, F1-score, accuracy, and AUC for classification models. Jupyter Notebook demonstrating the usage of classification metrics on multiple datasets (df_a, df_b, and df_c). Regression Metrics Custom implementation of Mean Squared Error (MSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) for regression models. Jupyter Notebook showcasing the application of regression metrics on the df_d dataset.

Requirements: Python 3.x NumPy pandas tqdm (for progress bars in threshold calculation)