query: What is a good oob score for random forests with sklearn, three-class classification?
positive_passages.docid: 1265
positive_passages.text: Is my model any good, based on the diagnostic metric ($R^2$/ AUC/ accuracy/ RMSE etc.) value?
negative_passages.docid: 20531
negative_passages.text: Why is accuracy not the best measure for assessing classification models?
negative_passages.docid: 103456
negative_passages.text: Non-perpendicular hyperplane decision trees
negative_passages.docid: 51420
negative_passages.text: Evaluating multiclass imbalanced problem per class
negative_passages.docid: 66215
negative_passages.text: Training a decision tree against unbalanced data
negative_passages.docid: 90482
negative_passages.text: Random forest regression not predicting higher than training data
negative_passages.docid: 74524
negative_passages.text: How to test accuracy of an unsupervised clustering model output?
negative_passages.docid: 144805
negative_passages.text: Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?
negative_passages.docid: 110124
negative_passages.text: When does decision tree perform better than the neural network?
negative_passages.docid: 26149
negative_passages.text: Logistic regression vs. LDA as two-class classifiers
negative_passages.docid: 84407
negative_passages.text: How to judge if a supervised machine learning model is overfitting or not?
negative_passages.docid: 12028
negative_passages.text: Do we have to tune the number of trees in a random forest?
negative_passages.docid: 104882
negative_passages.text: Relative importance of a set of predictors in a random forests classification in R
negative_passages.docid: 130691
negative_passages.text: Compare classifiers based on AUROC or accuracy?
negative_passages.docid: 67179
negative_passages.text: AUC and class imbalance in training/test dataset
negative_passages.docid: 132761
negative_passages.text: independent variables in Random forest model
negative_passages.docid: 113807
negative_passages.text: Testing Classification on Oversampled Imbalance Data