The random forest algorithm for statistical learning
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Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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- Teacher spread
- 0.241 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a classification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.
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The record
- Venue
- The Stata Journal Promoting communications on statistics and Stata
- Topic
- Financial Distress and Bankruptcy Prediction
- Field
- Business, Management and Accounting
- Canadian institutions
- University of Waterloo
- Funders
- —
- Keywords
- Random forestComputer scienceKey (lock)Artificial intelligenceCredit cardMachine learningAlgorithmStatistical learningWorld Wide Web
- Has abstract in OpenAlex
- yes