query: What is acceptable in a prediction model accuracy?
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: 102680
negative_passages.text: How do you report percentage accuracy for glmnet logistic regression?
negative_passages.docid: 87472
negative_passages.text: Calculating ratio of sample data used for model fitting/training and validation
negative_passages.docid: 102744
negative_passages.text: Evaluating models by Loglogss, AUC, and Accuracy
negative_passages.docid: 111170
negative_passages.text: Testing large dataset for normality - how and is it reliable?
negative_passages.docid: 63049
negative_passages.text: Train loss and accuracy
negative_passages.docid: 157675
negative_passages.text: Prediction interval on untransformed scale
negative_passages.docid: 48849
negative_passages.text: How to compare (probability) predictive ability of models developed from logistic regression?
negative_passages.docid: 149213
negative_passages.text: How confident is my model? Is there a way to know?
negative_passages.docid: 135987
negative_passages.text: Better accuracy with validation set than test set
negative_passages.docid: 110802
negative_passages.text: Model selection with low N?
negative_passages.docid: 153285
negative_passages.text: Is hyperparameter tuning on sample of dataset a bad idea?
negative_passages.docid: 40629
negative_passages.text: How to do a bias-variance analysis on a machine learning modelling process
negative_passages.docid: 5445
negative_passages.text: Obtaining a formula for prediction limits in a linear model (i.e.: prediction intervals)
negative_passages.docid: 142752
negative_passages.text: Should quantitative predictors be transformed  to be normally distributed?
negative_passages.docid: 49594
negative_passages.text: Measuring accuracy of a logistic regression-based model
negative_passages.docid: 131097
negative_passages.text: Checking residuals for normality in generalised linear models