Most popular metrics used to evaluate object detection algorithms.

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

Unofficial Python implementation of "Precision and Recall for Time Series".

Evaluation of 3D detection and diagnosis performance —geared towards prostate cancer detection in MRI.

Movie recommendation system with Python. Implements content-based filtering (TF-IDF + cosine similarity), collaborative filtering with matrix factorization (TruncatedSVD), and a hybrid approach. Evaluates with Precision@K, Recall@K, and NDCG. Includes rating distribution plots, top movies, and sample recommendations.

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

Time-series Aware Precision and Recall for Evaluating Anomaly Detection Methods

Machine learning utility functions and classes.

This is the official implementation for the Generative Modeling Density Alignment (GMDA). This work was presented in the paper "Frugal Generative Modeling for Tabular Data" at ECML 2024.

Classification Metric Manager is metrics calculator for machine learning classification quality such as Precision, Recall, F-score, etc.

Evaluate a detection model performance

Class-conditional BigGAN-style training on an ImageNet-10 subset with truncation sweeps and rigorous evaluation (FID, Precision/Recall, PRDC), plus a Gradio demo

This repository trains and evaluates three CNN models on MNIST, providing performance comparisons and 5 unique visualizations.

AUC curve metrics example

End-to-end Python baseline for intrusion/anomaly detection on network flows: LogReg/RF + OCSVM/IsolationForest, ROC-PR, thresholds.

ℹ️ Information Retrieval models implemented in Python

Objective: Reduce default rate among approved loans while maintaining healthy approval rates.

Companion Code for the Medium Article on top Python Data Science Interview Questions.