- Improving model performance through human participation - Apr 23, 2021.
Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.
Data Science Platform, Humans, Machine Learning, Model Performance, Precision, Recall
- Metric Matters, Part 1: Evaluating Classification Models - Mar 16, 2021.
You have many options when choosing metrics for evaluating your machine learning models. Select the right one for your situation with this guide that considers metrics for classification models.
Accuracy, Classification, Metrics, Precision, Recall, ROC-AUC
Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall - Feb 19, 2021.
This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.
Accuracy, Confusion Matrix, Deep Learning, Metrics, Precision, Recall
How to Evaluate the Performance of Your Machine Learning Model - Sep 3, 2020.
You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.
Accuracy, Confusion Matrix, Machine Learning, Precision, Predictive Modeling, Recall, ROC-AUC
21 Must-Know Data Science Interview Questions and Answers - Feb 11, 2016.
KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.
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Bootstrap sampling, Data Science, Interview Questions, Kirk D. Borne, Precision, Recall, Regularization, Yann LeCun
