368 search results for "PCA"

What are the Best Machine Learning Packages in R?

June 6, 2016
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Image 13

Guest post by Khushbu Shah The most common question asked by prospective data scientists is – “What is the best programming language for Machine Learning?” The answer to this question always results in a debate whether to choose R, Python or MATLAB for Machine Learning. Nobody can, in reality, answer the question as to whether Python or R is best...

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Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

June 5, 2016
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Building the Data Matrix for the Task at Hand and Analyzing Jointly the Resulting Rows and Columns

Someone decided what data ought to go into the matrix. They placed the objects of interest in the rows and the features that differentiate among those objects into the columns. Decisions were made either to collect information or to store what was gathered for other purposes (e.g., data mining).A set of mutually constraining choices...

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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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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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Tutorial: GitHub for Data Scientists without the Terminal

May 21, 2016
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Git and GitHub are indispensable tools for anyone analysing data, developing software or disseminating results. Originally designed for software engineers, GitHub is now widely used in many disciplines, especially for researchers in academia. Having a source code management software such as GitHub to host your code and have detailed project documentation is a huge step

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Installing WVPlots and “knitting R markdown”

May 20, 2016
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Installing WVPlots and “knitting R markdown”

Some readers have been having a bit of trouble using devtools to install WVPlots. I thought I would write a note with a few instructions to help. These are things you should not have to do often, and things those of us already running R have stumbled through and forgotten about. First you will need … Continue reading...

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IP string to integer conversion with Rcpp

May 19, 2016
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IP address conversion At work I recently had to match data on IP addresses and some fuzzy timestamp matching – a mess, to say the least. But before I could even tackle that problem, one dataset had the IPs stored as a character (e.g. 10.0.0.0), while the other dataset had the IP addresses converted as integers (e.g. 167772160). Storing IPs as...

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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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“Data Mining with R” Course | May 17-18

May 9, 2016
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Discover Data Mining with R: find patterns in large data sets using the R tools for Dimensionality Reduction, Clustering, Classification and Prediction. The post "Data Mining with R" Course | May 17-18 appeared first on MilanoR.

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