classification-metrics
There are 16 repositories under classification-metrics topic.
NannyML/The-Little-Book-of-ML-Metrics
The book every data scientist needs on their desk.
Mr-TalhaIlyas/Evaluation-Metrics-Package-Tensorflow-PyTorch-Keras
ML/CNN Evaluation Metrics Package
thecocolab/data-imbalance
Evaluating the effect of data balance on different classification metrics
Thehunk1206/Classical-ML-Algorithms
Collection of some classical Machine learning Algorithms.
AhmetZamanis/UsedCarKicksClassification
Imbalanced classification with scikit-learn and PyTorch Lightning.
ajitsingh98/Evaluation-Metrics-In-Machine-Learning-Problems-Python
evaluation metrics implementation in Python from scratch
barzansaeedpour/binary-classification-metrics
This repository provides essential tools and metrics for evaluating binary classification models, aiding researchers and data scientists in their model assessment
Jakub-Espandr/MetriCalc
A modern, cross-platform desktop app for calculating classification metrics from confusion matrices. Includes XLSX export, real-time language switching, and batch processing with responsive UI.
niaj-a/Machine-Learning
Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.
s0nya21/Loan-Approval-Prediction
When it comes to deciding whether the applicant’s profile is relevant to be granted with loan or not,banks have to look after many aspects. Predicting loan approval is a common application of machine learning in the financial industry.
AshiniAnantharaman/Bank_Customer_Churn_Prediction
This project is used to predict the customer churn based on various features using an artificial neural network
Hands-On-Fraud-Analytics/Chapter-18-Classification-Techniques-For-Fraud-Detection
Classification-Techniques-For-Fraud-Detection
AyanQuadri/ML-Lab
Machine Learning Algorithms
francescopiocirillo/bit-decoding-sgd-logistic-regression
📶 Logistic regression classifier for bit decoding in binary vectors using stochastic gradient descent (SGD). Features performance evaluation, probabilistic modeling, confusion matrix analysis, and classification error interpretation. Developed in Python with Jupyter Notebook.