What Is Oob_Score
The Oob_Score Is Computed as the Number of Correctly Predicted Rows from the out-of-Bag Sample. And. Oob Error Is the Number of Wrongly Classifying the Oob...
The OOB_score is computed as the number of correctly predicted rows from the out-of-bag sample. And. OOB Error is the number of wrongly classifying the OOB Sample.
What is Oob sample?
Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). Bagging uses subsampling with replacement to create training samples for the model to learn from.
What is a good out-of-bag score?
Most of the features have shown negligible importance – the mean is about 5%, a third of them is of importance 0, a third of them is of importance above the mean. However, perhaps the most striking fact is the oob (out-of-bag) score: a bit less than 1%.
James Sterling reports on renewable energy developments, climate policy, ecological conservation, and green tech innovations around the globe.
