query: Independent assessment of a selected model
positive_passages.docid: 54302
positive_passages.text: Cross-Validation in plain english?
negative_passages.docid: 71285
negative_passages.text: How is the lasso orthogonal design case solution derived?
negative_passages.docid: 2138
negative_passages.text: Modelling when the dependent variable has a "cut-off"
negative_passages.docid: 49896
negative_passages.text: A criterion for independence based on Characteristic function
negative_passages.docid: 36989
negative_passages.text: Determining functions from specifications...
negative_passages.docid: 114625
negative_passages.text: Model selection and cross-validation: The right way
negative_passages.docid: 42824
negative_passages.text: understanding the documentation of the standard evaluation sequence: own values
negative_passages.docid: 141640
negative_passages.text: Sklearn - Choosing the right model for supervised learning/classification task
negative_passages.docid: 148673
negative_passages.text: Comparison of log-likelihood of two non-nested models
negative_passages.docid: 65789
negative_passages.text: Independence of thermodynamic variables
negative_passages.docid: 55556
negative_passages.text: Should control variables be included in model if statistically insignificant?
negative_passages.docid: 102033
negative_passages.text: Separate Models vs Flags in the same model
negative_passages.docid: 145538
negative_passages.text: Model as multiple unique thin sheets
negative_passages.docid: 107299
negative_passages.text: Working with systems with Perfect Multicollinearity
negative_passages.docid: 20531
negative_passages.text: Why is accuracy not the best measure for assessing classification models?
negative_passages.docid: 22557
negative_passages.text: Choosing the best model from among different "best" models
negative_passages.docid: 34923
negative_passages.text: How does leave-one-out cross-validation work? How to select the final model out of $n$ different models?