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.

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

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

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.

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

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.

Report various statistics stemming from a confusion matrix in a tidy fashion. 🎯

A hands-on lab showing how “improving” a single metric (AUC/accuracy/F1) can worsen real-world outcomes. Includes metric audits, slice checks, cost-sensitive evaluation, threshold tuning, and decision policies you can defend, so dashboards don’t quietly ship bad decisions.

ML/CNN Evaluation Metrics Package

LSTM based model for Named Entity Recognition Task using pytorch and GloVe embeddings

The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidates more likely to have the visa certified.

BGU, Information Retrieval final project. Search-engine, Wikipedia corpus.

An information retrieval system which consists of various techniques' implementations like indexing, tokenization, stopping, stemming, page ranking, snippet generation and evaluation of results

ML-FinFraud-Detector is a machine learning project for detecting financial transaction fraud. Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection. Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.

📊Course 3: Machine Learning Specialization course of Coursera by the University of Washington on Classification

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 problem using multiple ML Algorithms

Developed a Convolutional Neural Network based on VGG16 architecture to diagnose COVID-19 and classify chest X-rays of patients suffering from COVID-19, Ground Glass Opacity and Viral Pneumonia. This repository contains the link to the dataset, python code for visualizing the obtained data and developing the model using Keras API.

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

Resample precision-recall curves correctly!

Calculating precision recall f1-score for gender classification methods

Built a fraud detection system to handle an imbalanced credit card transaction dataset using SMOTE and NearMiss for data balancing. Trained multiple models, including Logistic Regression, SVM, Random Forest, and a Neural Network, to detect fraud accurately. Evaluated performance using Precision-Recall AUC, F1-score, and ROC-AUC

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Learn ML evaluation metrics from scratch! Beginner-friendly Jupyter notebooks with code, visuals, and analogies - all pre-run with outputs. Master accuracy, F1, RMSE, ROC/AUC, cross-validation. Just read or run yourself. Includes challenge exercises. Perfect for ML beginners!

Trained MATLAB models for 82% precision/80% recall, optimized with blob analysis for 25% performance boost. User-friendly alarm system with 500+ engaged users.

Evaluate a detection model performance

Machine learning project for classifying cybersecurity incidents (TP, BP, FP) using the GUIDE dataset. Includes data preprocessing, feature engineering, model benchmarking, and evaluation with macro-F1, precision, and recall. Supports SOC automation, threat detection, and enterprise security management.