precision-recall

rafaelpadilla/review_object_detection_metrics

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.

deadskull7/Pneumonia-Diagnosis-using-XRays-96-percent-Recall

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.

CompML/PRTS

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

DIAGNijmegen/picai_eval

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

saurf4ng/TaPR

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

stefmolin/ml-utils

Machine learning utility functions and classes.

m-clark/confusionMatrix

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

baaraban/pytorch_ner

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

Mr-TalhaIlyas/Evaluation-Metrics-Package-Tensorflow-PyTorch-Keras

ML/CNN Evaluation Metrics Package

rochitasundar/Customer-profiling-using-ML-EasyVisa

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.

mayankchaudhary26/Classification

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

parshva45/Information-Retrieval-System

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

neeraj1397/Radiography-Based-Diagnosis-Of-COVID-19-Using-Deep-Learning

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.

OmerHanan1/IR-final-project

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

VivekSagarSingh/Probability-of-Credit-card-Default

Classification problem using multiple ML Algorithms

WINKAM/Classification-Metrics-Manager

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

ablacan/gmda

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.

ameya98/pr2roc

Resample precision-recall curves correctly!

avid7-tech/Predicting-stars-galaxies-and-quasars-using-ML-algorithms

This repository contains code for classifying galaxies into three classes: Galaxy, Quasar, and Star, using machine learning techniques. The dataset used in this project is the Sloan Digital Sky Survey (SDSS) dataset.

RobertRusev/ML-FinFraud-Detector

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.

zakarich/TF-IDF-cranDataSet

ash-0521/Abandoned-Object-Detection-in-crowded-environment-using-MATLAB

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

chandru-engineer/MNIST-Classification

In this project, the numeric digits are classified by using deep learning algorithm.

gouravaich/finding-donors-for-charity

Apply supervised machine learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause

mandyiv/Easy-Visa-Project

The objective of this analysis is to find patterns within the dataset to gain further understanding of the data and leverage it to choose a machine learning algorithm that can recommend a suitable profile for the applicants whose visa should be certified or denied

prakharpartha/AmEx--Analyse-This-2018

Amex Analyze This is a data science competition held by American Express across all the Indian Institute of Technology Institutes across India. I had participated in this competition in 2018, it was based on predictive modelling where we need to train a model to solve a bank problem - Analyze This 2018

riccardoprosdocimi/ml-predictive-maintenance

This repository contains code and documentation for a machine learning project focused on predictive maintenance in industrial machinery. The project explores the development of a comprehensive predictive maintenance system using various machine learning techniques.

rochitasundar/Classification-booking-cancelation-prediction-StarHotels

The aim is to develop an ML- based predictive classification model (logistic regression & decision trees) to predict which hotel booking is likely to be canceled. This is done by analysing different attributes of customer's booking details. Being able to predict accurately in advance if a booking is likely to be canceled will help formulate profitable policies for cancelations & refunds.

SaadTariq01DataAnalyst/Prediction-of-Bank-Churn-Customer

The goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them

Xu-Justin/detection-benchmark

Evaluate a detection model performance

alaazamelDev/text-based-search-engine

Implementation of a search engine using TF-IDF and Word Embedding-based vectorization techniques for efficient document retrieval

Niteshchawla/OLA-EnsembleLearning

Recruiting and retaining drivers is seen by industry watchers as a tough battle for Ola. Churn among drivers is high and it’s very easy for drivers to stop working for the service on the fly or jump to Uber depending on the rates.

kushagra3204/Text-Classification-Sentiment-Analysis

Text Classification for Sentiment Analysis using Multinomial Naive Bayes in C++

LegallyNotBlonde/credit-risk-classification

Built a Logistic Regression model to predict loan risk, focusing on credit risk management with precision, recall, and F1-score evaluation

Zen204/-My-eCornell-Portfolio

Includes all of my Jupyter Notebook assignments from MIT's Break Through AI/ML Machine Learning Foundations Program.