Posts Tagged ‘ Tutorials ’

y-aware scaling in context

June 22, 2016
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Nina Zumel introduced y-aware scaling in her recent article Principal Components Regression, Pt. 2: Y-Aware Methods. I really encourage you to read the article and add the technique to your repertoire. The method combines well with other methods and can drive better predictive modeling results. From feedback I am not sure everybody noticed that in … Continue reading y-aware scaling in context

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Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

June 9, 2016
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Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

Short form: Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time. Part 1: the proper preparation of data (including scaling) and use of principal components analysis (particularly for supervised learning or regression). Part 2: the introduction of y-aware scaling to direct the … Continue reading Why you should read Nina Zumel’s 3 part series on principal components…

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A demonstration of vtreat data preparation

June 2, 2016
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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 … Continue reading A demonstration of vtreat data preparation

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On ranger respect.unordered.factors

May 30, 2016
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On ranger respect.unordered.factors

It is often said that “R is its packages.” One package of interest is ranger a fast parallel C++ implementation of random forest machine learning. Ranger is great package and at first glance appears to remove the “only 63 levels allowed for string/categorical variables” limit found in the Fortran randomForest package. Actually this appearance is … Continue reading On ranger respect.unordered.factors

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Principal Components Regression, Pt. 2: Y-Aware Methods

May 23, 2016
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Principal Components Regression, Pt. 2: Y-Aware Methods

In our previous note, we discussed some problems that can arise when using standard principal components analysis (specifically, principal components regression) to model the relationship between independent (x) and dependent (y) variables. In this note, we present some dimensionality reduction techniques that alleviate some of those problems, in particular what we call Y-Aware Principal Components … Continue reading Principal Components Regression, Pt. 2: Y-Aware Methods

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Principal Components Regression, Pt.1: The Standard Method

May 17, 2016
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Principal Components Regression, Pt.1: The Standard Method

In this note, we discuss principal components regression and some of the issues with it: The need for scaling. The need for pruning. The lack of “y-awareness” of the standard dimensionality reduction step. The purpose of this article is to set the stage for presenting dimensionality reduction techniques appropriate for predictive modeling, such as y-aware … Continue reading Principal Components Regression, Pt.1: The Standard Method

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Coming up: principal components analysis

May 7, 2016
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Coming up: principal components analysis

Just a “heads-up.” I’ve been editing a two-part three-part series Nina Zumel is writing on some of the pitfalls of improperly applied principal components analysis/regression and how to avoid them (we are using the plural spelling as used in following Everitt The Cambridge Dictionary of Statistics). The series is looking absolutely fantastic and I think … Continue reading Coming up: principal components analysis

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vtreat cross frames

May 5, 2016
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vtreat cross frames

vtreat cross frames John Mount, Nina Zumel 2016-05-05 As a follow on to “On Nested Models” we work R examples demonstrating “cross validated training frames” (or “cross frames”) in vtreat. Consider the following data frame. The outcome only depends on the “good” variables, not on the (high degree of freedom) “bad” variables. Modeling such a … Continue reading vtreat cross frames

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On Nested Models

April 26, 2016
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On Nested Models

We have been recently working on and presenting on nested modeling issues. These are situations where the output of one trained machine learning model is part of the input of a later model or procedure. I am now of the opinion that correct treatment of nested models is one of the biggest opportunities for improvement … Continue reading On Nested Models

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Improved vtreat documentation

April 17, 2016
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Improved vtreat documentation

Nina Zumel has donated some time to greatly improve the vtreat R package documentation (now available as pre-rendered HTML here). vtreat is an R data.frame processor/conditioner package that helps prepare real-world data for predictive modeling in a statistically sound manner. Even with modern machine learning techniques (random forests, support vector machines, neural nets, gradient boosted … Continue reading Improved vtreat documentation

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