Introduction | Example 1 | Example 2 | Example 3 | Example 4 | Example 5 | Reversing Transforms | Package entry points | Conclusion

Scatterplots | Hexbin Plot | Gain Curves | ROC Plots | Precision-Recall-Threshold Plot | Threshold Plot | Double Density Plot | Double Histogram Plot | Cleveland Style Dotplots | Shadow Plots | Shadow Histogram | ScatterBox Plots | Discrete Distribution Plot | Distribution and Count Plots | Compare observations to a normal distribution with the same mean and standard deviation. | Compare "coin flip" observations to a binomial distribution | Compare observed rate data to a beta distribution with same mean and standard deviation | Smoothed Scatterplots | Density Plot with Shaded Tail | Density Plot with Shaded Central Region

String-interpolation | Quasi-quotation | Conclusion

Implementation details | More

When the target to predict is categorical | When the target to predict is numeric | When there is no supplied target to predict | Restricting to Specific Variable Types | Overall | Links

The Wrong Way | The Right Way: A Calibration Set | Another Right Way: Cross-Validation | The intuition | Cross-Validation and vtreat: The cross-frame. | Example

vtreat data set splitting | Motivation | Examples | Implementations | Conclusion

The Data | Partitioning the Data for Modeling | Arbitrary Partition | Group-preserving, y-stratified Partition

Bad Practice: Using the same data to treat and to train | What went wrong? | Correct Practice 1/2: Use different data to treat and train | Correct Practice 2/2: Use simulated out of sample methods (cross methods)

Note

Categorical outcome mode "catScaling=TRUE" | PCA/PCR

Introduction | sql_node() | Composing nodes | A non-trivial example | Working with sets of columns | SQL first | Ad-hoc mode | Table descriptions | Helper functions and notation | Conclusion | See also | Appendix: Always clean up on the way out

Introduction | Table descriptions | Operators | Non-SQL nodes | Standard interfaces | Assignment partitioning | Performance | Conclusion | See also | Appendix: Always clean up on the way out

Appendix

Basic unpack() example | Reusing the list names as variables | Unpacking only parts of a list | unpack checks for unknown elements | Other multiple assignment packages | zeallot | vadr | tidytidbits

Dot-Pipe: an S3 Extensible Pipe for R

Converting Between Value Oriented and Code Capturing Interfaces in R

Basic wrapr | Dereferencing and function evaluation | "wrapr_applicable" | Conclusion

The substitution modes | Language substitution (subsMethod='langsubs') | String substitution (subsMethod='stringsubs') | Substitute substitution (subsMethod='subsubs') | Conclusion