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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. It’s Time to Finally Memorize Those Dang Classification Metrics!

   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.

   2024-06-24 Tags: classification, metrics, machine learning, data science, precision, recall, accuracy, roc, auc by klotz
3. Evaluating Time Series Anomaly Detection: Proximity-Aware Time Series Anomaly Evaluation (PATE)

   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.

   2024-05-27 Tags: time series, anomaly detection, pate, precision, recall, macos, production engineering by klotz
4. 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
5. XGBoost: Intro, Step-by-Step Implementation, and Performance Comparison

   - Extreme Gradient Boosting: A quick and reliable regressor and classifier
   - Summary: LightGBM is faster and better though XGBoost is close

   2023-09-29 Tags: machine learning, gofml, accuracy, precision, recall, xgboost, random forest, svm, k-nn, lightgbm, catboost, gradientboosting, adaboost by klotz
6. Identifying the Right Classification Metric for Your Task

   2021-11-10 Tags: classification_metrics, accuracy, precision, recall, f1-score, fβ-score, ; statistics, machine_learning by klotz
7. What does your classification metric tell about your data?

   2019-12-26 Tags: classification, metrics, precision, recall, f1, roc by klotz
8. “The 5 Classification Evaluation metrics every Data Scientist must know”

   2019-09-18 Tags: machine learning, classification, auc, f1, precision, recall, cross entropy by klotz
9. Why are Eight Bits Enough for Deep Neural Networks? | Pete Warden's blog

   2019-04-14 Tags: deep learning, hardware, floating point, precision, performance by klotz
10. “Beyond Accuracy: Precision and Recall”

    2018-08-03 Tags: precision, recall, statistics, machine learning by klotz

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