query: Predict real world data after modelling with scaled features
positive_passages.docid: 150930
positive_passages.text: ANN regression accuracy and loss stuck
negative_passages.docid: 6454
negative_passages.text: Perform feature normalization before or within model validation?
negative_passages.docid: 142623
negative_passages.text: scaling for SVM destroys my results
negative_passages.docid: 156972
negative_passages.text: Scale-invariant analysis of time series
negative_passages.docid: 15196
negative_passages.text: Why scaling is important for the linear SVM classification?
negative_passages.docid: 48283
negative_passages.text: Feature selection and cross-validation
negative_passages.docid: 37433
negative_passages.text: How to build a predictive model when more levels of a categorical predictor are possible than appear in the training data
negative_passages.docid: 140385
negative_passages.text: Practical thoughts on explanatory vs. predictive modeling
negative_passages.docid: 69637
negative_passages.text: Random Forests and data transformations
negative_passages.docid: 148679
negative_passages.text: Modelling with more variables than data points
negative_passages.docid: 25399
negative_passages.text: Predicting with both continuous and categorical features
negative_passages.docid: 27143
negative_passages.text: Detecting significant predictors out of many independent variables
negative_passages.docid: 131097
negative_passages.text: Checking residuals for normality in generalised linear models
negative_passages.docid: 17831
negative_passages.text: Training on the full dataset after cross-validation?
negative_passages.docid: 102744
negative_passages.text: Evaluating models by Loglogss, AUC, and Accuracy
negative_passages.docid: 69548
negative_passages.text: Simultaneously fitting multiple datasets
negative_passages.docid: 16519
negative_passages.text: Transformed data due to non-normal residuals - how to see if it actually improved the model?