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1. 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
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
3. 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

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