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)