Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , , , 9 Comments on R Tip: Use the vtreat Package For Data Preparation

R Tip: Use the vtreat Package For Data Preparation

If you are working with predictive modeling or machine learning in R this is the R tip that is going to save you the most time and deliver the biggest improvement in your results.

R Tip: Use the vtreat package for data preparation in predictive analytics and machine learning projects.

Vtreat

When attempting predictive modeling with real-world data you quickly run into difficulties beyond what is typically emphasized in machine learning coursework:

  • Missing, invalid, or out of range values.
  • Categorical variables with large sets of possible levels.
  • Novel categorical levels discovered during test, cross-validation, or model application/deployment.
  • Large numbers of columns to consider as potential modeling variables (both statistically hazardous and time consuming).
  • Nested model bias poisoning results in non-trivial data processing pipelines.

Any one of these issues can add to project time and decrease the predictive power and reliability of a machine learning project. Many real world projects encounter all of these issues, which are often ignored leading to degraded performance in production.

vtreat systematically and correctly deals with all of the above issues in a documented, automated, parallel, and statistically sound manner.

vtreat can fix or mitigate these domain independent issues much more reliably and much faster than by-hand ad-hoc methods.
This leaves the data scientist or analyst more time to research and apply critical domain dependent (or knowledge based) steps and checks.

If you are attempting high-value predictive modeling in R, you should try out vtreat and consider adding it to your workflow.

Continue reading R Tip: Use the vtreat Package For Data Preparation

Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , , , , , 3 Comments on A Theory of Nested Cross Simulation

A Theory of Nested Cross Simulation

[Reader’s Note. Some of our articles are applied and some of our articles are more theoretical. The following article is more theoretical, and requires fairly formal notation to even work through. However, it should be of interest as it touches on some of the fine points of cross-validation that are quite hard to perceive or discuss without the notational framework. We thought about including some “simplifying explanatory diagrams” but so many entities are being introduced and manipulated by the processes we are describing we found equation notation to be in fact cleaner than the diagrams we attempted and rejected.]

Please consider either of the following common predictive modeling tasks:

  • Picking hyper-parameters, fitting a model, and then evaluating the model.
  • Variable preparation/pruning, fitting a model, and then evaluating the model.

In each case you are building a pipeline where “y-aware” (or outcome aware) choices and transformations made at each stage affect later stages. This can introduce undesirable nested model bias and over-fitting.

Our current standard advice to avoid nested model bias is either:

  • Split your data into 3 or more disjoint pieces, such as separate variable preparation/pruning, model fitting, and model evaluation.
  • Reserve a test-set for evaluation and use “simulated out of sample data” or “cross-frame”/“cross simulation” techniques to simulate dividing data among the first two model construction stages.

The first practice is simple and computationally efficient, but statistically inefficient. This may not matter if you have a lot of data, as in “big data”. The second procedure is more statistically efficient, but is also more complicated and has some computational cost. For convenience the cross simulation method is supplied as a ready to go procedure in our R data cleaning and preparation package vtreat.

What would it look like if we insisted on using cross simulation or simulated out of sample techniques for all three (or more) stages? Please read on to find out.

CleanAllTheThings

Hyperbole and a Half copyright Allie Brosh (use allowed in some situations with attribution)

Edit: we are going to be writing on a situation of some biases that do leak into the cross-frame “new data simulation.” So think of cross-frames as bias (some small amount is introduced) / variance (reduced be appearing to have a full sized data set at all stages) trade-off.

Posted on Categories Statistics, TutorialsTags , , , , 5 Comments on Variables can synergize, even in a linear model

Variables can synergize, even in a linear model

Introduction

Suppose we have the task of predicting an outcome y given a number of variables v1,..,vk. We often want to “prune variables” or build models with fewer than all the variables. This can be to speed up modeling, decrease the cost of producing future data, improve robustness, improve explain-ability, even reduce over-fit, and improve the quality of the resulting model.

For some informative discussion on such issues please see the following:

In this article we are going to deliberately (and artificially) find and test one of the limits of the technique. We recommend simple variable pruning, but also think it is important to be aware of its limits.

Continue reading Variables can synergize, even in a linear model

Posted on Categories Computer Science, math programming, Statistics, TutorialsTags , , , 2 Comments on Variable pruning is NP hard

Variable pruning is NP hard

I am working on some practical articles on variable selection, especially in the context of step-wise linear regression and logistic regression. One thing I noticed while preparing some examples is that summaries such as model quality (especially out of sample quality) and variable significances are not quite as simple as one would hope (they in fact lack a lot of the monotone structure or submodular structure that would make things easy).

That being said we have a lot of powerful and effective heuristics to discuss in upcoming articles. I am going to leave such positive results for my later articles and here concentrate on an instructive technical negative result: picking a good subset of variables is theoretically quite hard. Continue reading Variable pruning is NP hard

Posted on Categories Administrativia, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , 1 Comment on vtreat version 0.5.26 released on CRAN

vtreat version 0.5.26 released on CRAN

Win-Vector LLC, Nina Zumel and I are pleased to announce that ‘vtreat’ version 0.5.26 has been released on CRAN.

‘vtreat’ is a data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.

(from the package documentation)

‘vtreat’ is an R package that incorporates a number of transforms and simulated out of sample (cross-frame simulation) procedures that can:

  • Decrease the amount of hand-work needed to prepare data for predictive modeling.
  • Improve actual model performance on new out of sample or application data.
  • Lower your procedure documentation burden (through ready vtreat documentation and tutorials).
  • Increase model reliability (by re-coding unexpected situations).
  • Increase model expressiveness (by allowing use of more variable types, especially large cardinality categorical variables).

‘vtreat’ can be used to prepare data for either regression or classification.

Please read on for what ‘vtreat’ does and what is new. Continue reading vtreat version 0.5.26 released on CRAN

Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , 8 Comments on A demonstration of vtreat data preparation

A demonstration of vtreat data preparation

This article is a demonstration the use of the R vtreat variable preparation package followed by caret controlled training.

In previous writings we have gone to great lengths to document, explain and motivate vtreat. That necessarily gets long and unnecessarily feels complicated.

In this example we are going to show what building a predictive model using vtreat best practices looks like assuming you were somehow already in the habit of using vtreat for your data preparation step. We are deliberately not going to explain any steps, but just show the small number of steps we advise routinely using. This is a simple schematic, but not a guide. Of course we do not advise use without understanding (and we work hard to teach the concepts in our writing), but want what small effort is required to add vtreat to your predictive modeling practice.

Continue reading A demonstration of vtreat data preparation

Posted on Categories Administrativia, Statistics, TutorialsTags , ,

Improved vtreat documentation

Nina Zumel has donated some time to greatly improve the vtreat R package documentation (now available as pre-rendered HTML here).

Chrome Vanadium Adjustable Wrench

vtreat is an R data.frame processor/conditioner package that helps prepare real-world data for predictive modeling in a statistically sound manner. Continue reading Improved vtreat documentation

Posted on Categories Statistics, TutorialsTags , ,

Prepping Data for Analysis using R

Nina and I are proud to share our lecture: “Prepping Data for Analysis using R” from ODSC West 2015.


Nina Zumel and John Mount ODSC WEST 2015

It is about 90 minutes, and covers a lot of the theory behind the vtreat data preparation library.

We also have a Github repository including all the lecture materials here. Continue reading Prepping Data for Analysis using R

Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , 7 Comments on vtreat up on CRAN!

vtreat up on CRAN!

Nina Zumel and I are proud to announce our R vtreat variable treatment library has just been accepted by CRAN!

IMG 6061 Continue reading vtreat up on CRAN!

Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, Statistics To English TranslationTags , , , , 9 Comments on How Do You Know if Your Data Has Signal?

How Do You Know if Your Data Has Signal?

NewImage
Image by Liz Sullivan, Creative Commons. Source: Wikimedia

An all too common approach to modeling in data science is to throw all possible variables at a modeling procedure and “let the algorithm sort it out.” This is tempting when you are not sure what are the true causes or predictors of the phenomenon you are interested in, but it presents dangers, too. Very wide data sets are computationally difficult for some modeling procedures; and more importantly, they can lead to overfit models that generalize poorly on new data. In extreme cases, wide data can fool modeling procedures into finding models that look good on training data, even when that data has no signal. We showed some examples of this previously in our “Bad Bayes” blog post.

In this latest “Statistics as it should be” article, we will look at a heuristic to help determine which of your input variables have signal. Continue reading How Do You Know if Your Data Has Signal?