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1. 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
2. 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
3. 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
4. What does your classification metric tell about your data?

   2019-12-26 Tags: classification, metrics, precision, recall, f1, roc by klotz
5. “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
6. “Beyond Accuracy: Precision and Recall”

   2018-08-03 Tags: precision, recall, statistics, machine learning by klotz
7. Mazda Recall Information | Mazda USA

   2016-06-30 Tags: mazda, recall, car by klotz

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