query: Should one merge training and testing sets when predicting a class of a new sample?
positive_passages.docid: 17831
positive_passages.text: Training on the full dataset after cross-validation?
negative_passages.docid: 84407
negative_passages.text: How to judge if a supervised machine learning model is overfitting or not?
negative_passages.docid: 14138
negative_passages.text: Normality test with large data set
negative_passages.docid: 6454
negative_passages.text: Perform feature normalization before or within model validation?
negative_passages.docid: 107720
negative_passages.text: Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests
negative_passages.docid: 158440
negative_passages.text: Supervised learning with uncertain data?
negative_passages.docid: 77851
negative_passages.text: Should we apply normalization to test data as well?
negative_passages.docid: 87472
negative_passages.text: Calculating ratio of sample data used for model fitting/training and validation
negative_passages.docid: 141640
negative_passages.text: Sklearn - Choosing the right model for supervised learning/classification task
negative_passages.docid: 116861
negative_passages.text: Data augmentation techniques for general datasets?
negative_passages.docid: 70045
negative_passages.text: Can we use bootstrap samples that are smaller than original sample?
negative_passages.docid: 78589
negative_passages.text: Why mini batch size is better than one single "batch" with all training data?
negative_passages.docid: 18820
negative_passages.text: For imbalanced classification, should the validation dataset be balanced?
negative_passages.docid: 11067
negative_passages.text: How to choose a predictive model after k-fold cross-validation?
negative_passages.docid: 58047
negative_passages.text: Decision Trees and Regression - Can predicted values be outside range of training data?
negative_passages.docid: 87047
negative_passages.text: For model selection/comparison, what kind of test should I use?
negative_passages.docid: 87457
negative_passages.text: With k-fold cross-validation, do you average all $k$ models to build the final model?