2206 search results for "regression"

Introduction to R for Data Science :: Session 8 [Intro to Text Mining in R, ML Estimation + Binomial Logistic Regression]

June 21, 2016
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Introduction to R for Data Science :: Session 8 [Intro to Text Mining in R, ML Estimation + Binomial Logistic Regression]

Welcome to Introduction to R for Data Science, Session 8: Intro to Text Mining in R, ML Estimation + Binomial Logistic Regression [Web-scraping with tm.plugin.webmining. The tm package corpora structures: assessing document metadata and content. Typical corpus transformations and Term-Document Matrix production. A simple binomial regression model with tf-idf scores as features and its shortcommings due to sparse data....

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R for Publication by Page Piccinini: Lesson 4 – Multiple Regression

June 13, 2016
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R for Publication by Page Piccinini: Lesson 4 – Multiple Regression

Introduction Today we’ll see what happens when you have not one, but two variables in your model. We will also continue to use some old and new dplyr calls, as well as another parameter for our ggplot2 figure. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done Lesson 4: Multiple...

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Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R  + Categorical Predictors, Partial and Part Correlation]

June 9, 2016
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Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R  + Categorical Predictors, Partial and Part Correlation]

Welcome to Introduction to R for Data Science Session 7: Multiple Regression + Dummy Coding, Partial and Part Correlations [Multiple Linear Regression in R. Dummy coding: various ways to do it in R. Factors. Inspecting the multiple regression model: regression coefficients and their interpretation, confidence intervals, predictions. Introducing {lattice} plots + ggplot2. Assumptions: multicolinearity and testing it from the...

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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...

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R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

June 9, 2016
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R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

Today we’ll be moving from linear regression to logistic regression. This lesson also introduces a lot of new dplyr verbs for data cleaning and summarizing that we haven’t used before. Once again, I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go Lesson 3: Logistic...

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Introduction to R for Data Science :: Session 6 [Linear Regression Model in R  + EDA, and Normality Tests]

June 6, 2016
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Introduction to R for Data Science :: Session 6 [Linear Regression Model in R  + EDA, and Normality Tests]

Welcome to Introduction to R for Data Science Session 6: Linear Regression + EDA, and Normality tests The course is co-organized by Data...

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R for Publication by Page Piccinini: Lesson 2 – Linear Regression

June 2, 2016
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R for Publication by Page Piccinini: Lesson 2 – Linear Regression

This is our first lesson where we actually learn and use a new statistic in R. For today’s lesson we’ll be focusing on linear regression. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go back and do it. By the Lesson 2: Linear...

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Principal Components Regression in R: Part 3

May 31, 2016
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Principal Components Regression in R: Part 3

by John Mount Ph. D. Data Scientist at Win-Vector LLC In her series on principal components analysis for regression in R, Win-Vector LLC's Dr. Nina Zumel broke the demonstration down into the following pieces: Part 1: the proper preparation of data and use of principal components analysis (particularly for supervised learning or regression). Part 2: the introduction of y-aware...

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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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