Entries by tag: model-evaluation

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Paper Summary: The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets  29 Mar 2021    paper-summary model-evaluation

Summary of the 2015 article "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets" by Saito and Hemsmeier. Read More ›

Evaluation Metrics for Ranking problems: Introduction and Examples  24 Jan 2019    machine-learning model-evaluation

Explanation and examples on how to calculate the performance of ranked predictions for machine learning. Read More ›

Introduction to AUC and Calibrated Models with Examples using Scikit-Learn  15 Apr 2018    machine-learning data-science model-evaluation

Inspired by a podcast episode by Linear Digressions, which talks about what AUC is and what it is not and why you need well calibrated models if you want to treat their outputs as probabilities. Read More ›

Evaluation Metrics for Classification Problems: Quick Examples + References  31 Aug 2017    data-newsletter-4 machine-learning model-evaluation

There are multiple ways to measure your model's performance in machine learning, depending upon what objectives you have in mind. Some of the most important are Accuracy, Precision, Recall, F1 and AUC. Read More ›