14 Great Articles About Cross-Validation, Model Fitting and Selection

14 Great Articles About Cross-Validation, Model Fitting and Selection

Cross-validation is a technique used to assess the accuracy of a predictive model, based on training set data. It splits the training sets into  test and control sets. The test sets are used to fine-tune the model to increase performance (better classification rate or reduced errors in prediction) and the control sets are used to simulate how the model would perform outside the training set. The control and test sets must be carefully chosen for this method to make sense.
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Cross-validation in R: a do-it-yourself and a black box approach 
Choice of K in K-fold Cross Validation for Classification in Financial Market 
Cross-Validation: Concept and Example in R 
How to Train a Final Machine Learning Model  +
How to create a Best-Fitting regression model? 
Underfitting/Overfitting Problem in M/C learning 
SVM in Practice 
Machine Learning : Few rarely shared trade secrets 
What causes predictive models to fail – and how to fix it? 
Use PRESS, not R squared to judge predictive power of regression 
Stacking models for improved predictions 
Data Science Dictionary 
Handling Imbalanced data when building regression models 
11 Important Model Evaluation Techniques Everyone Should Know 

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Link: 14 Great Articles About Cross-Validation, Model Fitting and Selection