query: How to judge whether model is overfitting or not
positive_passages.docid: 84407
positive_passages.text: How to judge if a supervised machine learning model is overfitting or not?
negative_passages.docid: 131006
negative_passages.text: What is a "model" in machine learning?
negative_passages.docid: 28578
negative_passages.text: How do I test a nonlinear association?
negative_passages.docid: 1731
negative_passages.text: Estimate model with dummies for whole sample versus model without dummies for each subsample
negative_passages.docid: 104487
negative_passages.text: Finding Bias$(s^2)$ in incorrect linear model
negative_passages.docid: 45291
negative_passages.text: Overmatching bias and confounding variables
negative_passages.docid: 139734
negative_passages.text: Choosing between uninformative beta priors
negative_passages.docid: 102983
negative_passages.text: (Why) do overfitted models tend to have large coefficients?
negative_passages.docid: 48916
negative_passages.text: Determing two Nonlinear model fits with 95% confidence bands from one data set
negative_passages.docid: 14
negative_passages.text: How to know that your machine learning problem is hopeless?
negative_passages.docid: 128261
negative_passages.text: Can you compare AIC values as long as the models are based on the same dataset?
negative_passages.docid: 36088
negative_passages.text: Class imbalance in Supervised Machine Learning
negative_passages.docid: 138454
negative_passages.text: Determining sample size for ML algorithm validation
negative_passages.docid: 1265
negative_passages.text: Is my model any good, based on the diagnostic metric ($R^2$/ AUC/ accuracy/ RMSE etc.) value?
negative_passages.docid: 97842
negative_passages.text: how to select the best non-linear model that represents the data?
negative_passages.docid: 135800
negative_passages.text: Validation of Models with Different Scale Data
negative_passages.docid: 57060
negative_passages.text: Is normalizing the features always good for classification?