2071 search results for "regression"

Understanding beta binomial regression (using baseball statistics)

May 31, 2016
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Understanding beta binomial regression (using baseball statistics)

Previously in this series: Understanding the beta distribution Understanding empirical Bayes estimation Understanding credible intervals Understanding the Bayesian approach to false discovery rates Understanding Bayesian A/B testing In this series we’ve been using the empirical Bayes method to estimate batting averages of baseball players. Empirical Bayes is useful here because when we...

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Principal Components Regression, Pt. 3: Picking the Number of Components

May 30, 2016
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Principal Components Regression, Pt. 3: Picking the Number of Components

In our previous note we demonstrated Y-Aware PCA and other y-aware approaches to dimensionality reduction in a predictive modeling context, specifically Principal Components Regression (PCR). For our examples, we selected the appropriate number of principal components by eye. In this note, we will look at ways to select the appropriate number of principal components in … Continue reading...

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Visualizing Bootrapped Stepwise Regression in R using Plotly

May 29, 2016
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We all have used stepwise regression at some point. Stepwise regression is known to be sensitive to initial inputs. One way to mitigate this sensitivity is to repeatedly run stepwise regression on bootstrap samples. R has a nice package called bootStepAIC() which (from its description) “Implements a Bootstrap procedure to investigate the variability of model

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End to end Logistic Regression in R

May 29, 2016
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End to end Logistic Regression in R

Logistic regression, or logit regression is a regression model where the dependent variable is categorical. I have provided code below to perform end-to-end logistic regression in R including data preprocessing, training and evaluation. The dataset used can be downloaded from here.

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Principal Components Regression in R: Part 2

May 24, 2016
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Principal Components Regression in R: Part 2

by John Mount Ph. D. Data Scientist at Win-Vector LLC In part 2 of her series on Principal Components Regression Dr. Nina Zumel illustrates so-called y-aware techniques. These often neglected methods use the fact that for predictive modeling problems we know the dependent variable, outcome or y, so we can use this during data preparation in addition to using...

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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...

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Visual contrast of two robust regression methods

Visual contrast of two robust regression methods

Robust regression For training purposes, I was looking for a way to illustrate some of the different properties of two different robust estimation methods for linear regression models. The two methods I’m looking at are: least trimmed squares, implemented as the default option in lqs() a Huber M-estimator, implemented as the default option in rlm() Both functions...

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Principal Components Regression in R, an operational tutorial

May 17, 2016
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Principal Components Regression in R, an operational tutorial

John Mount Ph. D. Data Scientist at Win-Vector LLC Win-Vector LLC's Dr. Nina Zumel has just started a two part series on Principal Components Regression that we think is well worth your time. You can read her article here. Principal Components Regression (PCR) is the use of Principal Components Analysis (PCA) as a dimension reduction step prior to linear...

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Principal Components Regression, Pt.1: The Standard Method

May 16, 2016
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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...

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Manipulate(d) Regression!

May 5, 2016
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Manipulate(d) Regression!

The R package ‘manipulate’ can be used to create interactive plots in RStudio. Though not as versatile as the ‘shiny’ package, ‘manipulate’ can be used to quickly add interactive elements to standard R plots. This can prove useful for demonstrating statistical concepts, especially to a non-statistician audience. The R code at the end of this

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