This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
OpenRecall is an open-source software that aims to be a privacy-focused alternative to Microsoft's Recall feature. It captures the user's digital history, processes text and images using OCR, and allows users to find specific information by searching for relevant keywords. Currently, it stores data locally but does not encrypt it. It is available for Windows, macOS, and Linux.
Web application that summarizes online content, automatically categorizes and interlinks it for easy rediscovery. Save time and build your knowledge base with Recall.
This article discusses the importance of understanding and memorizing classification metrics in machine learning. The author shares their own experience and strategies for memorizing metrics such as accuracy, precision, recall, F1 score, and ROC AUC.
The article discusses the challenges faced in evaluating anomaly detection in time series data and introduces Proximity-Aware Time series anomaly Evaluation (PATE) as a solution. PATE provides a weighted version of Precision and Recall curve and considers temporal correlations and buffer zones for a more accurate and nuanced evaluation.
Learn about the importance of evaluating classification models and how to use the confusion matrix and ROC curves to assess model performance. This post covers the basics of both methods, their components, calculations, and how to visualize the results using Python.
- Extreme Gradient Boosting: A quick and reliable regressor and classifier
- Summary: LightGBM is faster and better though XGBoost is close
- Summary: LightGBM is faster and better though XGBoost is close
