SemanticScuttle - klotz.me

Tags: evaluation* + recall* + precision* + machine learning* + accuracy*

0 bookmark(s) - Sort by: Date ↓ / Title /

1. Metrics to Evaluate a Classification Machine Learning Model

   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.

   2024-08-01 Tags: machine learning, classification, metrics, evaluation, precision, recall, f1-score, accuracy, alert rate, fraud detection, llm by klotz
2. Evaluating Classification Models: Understanding the Confusion Matrix and ROC Curves

   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.

   2024-04-08 Tags: machine learning, classification models, confusion matrix, roc curves, evaluation, true positives, true negatives, false positives, false negatives, accuracy, precision, recall, specificity, f1 score, balanced accuracy by klotz

Top of the page

First / Previous / Next / Last / Page 1 of 0