490 search results for "evaluation"

RSI(2) Evaluation

June 28, 2009
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RSI(2) Evaluation

Despite my best efforts, it's been a month since the last post of this series. The first post replicated this simple RSI(2) strategy from the MarketSci Blog using R. The second post showed how to replicate the strategy that scales in/out of RSI(2). ...

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Cross-Validation: Estimating Prediction Error

April 29, 2016
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Cross-Validation: Estimating Prediction Error

What is cross-validation? Cross-Validation is a technique used in model selection to better estimate the test error of a predictive model. The idea behind cross-validation is to create a number of partitions of sample observations, known as the validation sets, from the training data set. After fitting a model on to the training data, its Related Post

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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...

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How to sort a list of dataframes

April 13, 2016
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A method to gather data from different sources, sort them and keep a reference to the origin of each subset, plus some efficiency considerations The post How to sort a list of dataframes appeared first on MilanoR.

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Predicting Wine Quality with Azure ML and R

April 11, 2016
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Predicting Wine Quality with Azure ML and R

by Shaheen Gauher, PhD, Data Scientist at Microsoft In machine learning, the problem of classification entails correctly identifying to which class or group a new observation belongs, by learning from observations whose classes are already known. In what follows, I will build a classification experiment in Azure ML Studio to predict wine quality based on physicochemical data. Several classification...

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Supervised Machine Learning with R Workshop on April 30th

April 10, 2016
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Supervised Machine Learning with R Workshop on April 30th

Data Community DC and District Data Labs are hosting a Supervised Machine Learning with R workshop on Saturday April 30th. Come out and learn about R's capabilities for regression and classification, how to perform inference with these models, and how to use out-of-sample evaluation methods for your models!

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A quick introduction to machine learning in R with caret

April 6, 2016
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A quick introduction to machine learning in R with caret

If you’ve been using R for a while, and you’ve been working with basic data visualization and data exploration techniques, the next logical step is to start learning some machine learning. To help you begin learning about machine learning in R, I’m going to introduce you to an R package: the caret package. We’ll build The post

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Working with databases in R

April 2, 2016
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Working with databases in R

The dplyr package, which is one of my favorite R packages, works with in-memory data and with data stored in databases. In this extensive and comprehensive post, I will share my experience on using dplyr to work with databases. The basic functions of dplyr package are covered by Teja in another post at DataScience+ Using Related Post

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What’s new on CRAN: March 2016

March 31, 2016
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What’s new on CRAN: March 2016

by Joseph Rickert Packages continue to flood into CRAN at a rate the challenges the sanity of anyone trying to keep up with what's new. So far this month, more than 190 packages have been added. Here is a my view of what's interesting in this March madness. The launch_tutorial() function from the RtutoR package by Anup Nair launches...

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Real-time model scoring for streaming data – a prototype based on Oracle Stream Explorer and Oracle R Enterprise

March 30, 2016
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Real-time model scoring for streaming data – a prototype based on Oracle Stream Explorer and Oracle R Enterprise

Whether applied to manufacturing, financial services, energy, transportation, retail, government, security or other domains, real-time analytics is an umbrella term which covers a broad spectrum of capabilities (data integration, analytics, business intelligence) built on streaming input from multiple channels. Examples of such channels are: sensor data, log data, market data, click streams, social media and monitoring imagery. Key metrics...

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