Assessing Prediction Accuracy of Machine Learning Models
By: and and
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- Synopsis
- The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools are then described: mean squared error (MSE), mean absolute deviation (MAD), Brier score, and cross-entropy, true/false positives/negatives, the confusion matrix, true positive rate (sensitivity or recall), false negative rate (Type II error rate), precision, true negative rate (specificity), false positive rate (Type I error rate), receiver operating characteristic curve (ROC) and area under the curve (AUC), and precision-recall curve.
- Copyright:
- 2020
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 12 Pages
- Publisher:
- Harvard Business Publishing
- Date of Addition:
- 01/18/23
- Copyrighted By:
- President and Fellows of Harvard College
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
- Special Notes:
- Case.