459 search results for "evaluation"

Data Mining with R: Generate knowledge from data with the eoda R-Academy

October 21, 2015
By

The course “Data Mining with R”, which takes place from 9th to 10th November 2015 in Kassel, Germany, teaches the most important methods to expose correlations in data and inherent patterns. The wide set of methods can be used for almost every application area. From customer segmentation to timely recognition of machines, users gain knowledge

Read more »

Data Mining Standard Process across Organizations

October 18, 2015
By
Data Mining Standard Process across Organizations

Recently I have come across a term, CRISP-DM - a data mining standard. Though this process is not a new one but I felt every analyst should know about commonly used Industry wide process. In this post I will explain about different phases involved in creating a data mining solution. CRISP-DM, an acronym for Cross Industry Standard...

Read more »

Review: Data Mining with Rattle and R

October 7, 2015
By
Review: Data Mining with Rattle and R

I read Data Mining with Rattle and R by Graham Williams over a year ago. It's not a new book and I've just been tardy in writing up a review. That's not to say that I have not used the book in the interim: it's been on my desk at work ever since and I've The post

Read more »

Recruiting Analysts for dynamic cutting edge public sector team

October 3, 2015
By
Recruiting Analysts for dynamic cutting edge public sector team

The jobs Within the New Zealand Ministry of Business, Innovation and Employment, the Sector Trends team has recently secured resourcing for additional analysts on a range of statistical programmes. That’s the team that I usually manage, although for the next few months I’m doing a stint on a similar team, different topics. The formal details and position descriptions...

Read more »

A Simpler Explanation of Differential Privacy

October 2, 2015
By
A Simpler Explanation of Differential Privacy

Differential privacy was originally developed to facilitate secure analysis over sensitive data, with mixed success. It’s back in the news again now, with exciting results from Cynthia Dwork, et. al. (see references at the end of the article) that apply results from differential privacy to machine learning. In this article we’ll work through the definition … Continue reading...

Read more »

Notes on Multivariate Gaussian Quadrature (with R Code)

September 25, 2015
By
Notes on Multivariate Gaussian Quadrature (with R Code)

Statisticians often need to integrate some function with respect to the multivariate normal (Gaussian) distribution, for example, to compute the standard error of a statistic, or the likelihood function in of a mixed effects model. In many (most?) useful cases, these integrals are intractable, and must be approximated using computational methods. Monte-Carlo integration is one

Read more »

How do you know if your model is going to work?

September 22, 2015
By
How do you know if your model is going to work?

Authors: John Mount (more articles) and Nina Zumel (more articles). Our four part article series collected into one piece. Part 1: The problem Part 2: In-training set measures Part 3: Out of sample procedures Part 4: Cross-validation techniques “Essentially, all models are wrong, but some are useful.” George Box Here’s a caricature of a data … Continue reading...

Read more »

How do you know if your model is going to work? Part 4: Cross-validation techniques

September 22, 2015
By
How do you know if your model is going to work? Part 4: Cross-validation techniques

by John Mount (more articles) and Nina Zumel (more articles). In this article we conclude our four part series on basic model testing. When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it's better than the models that you rejected? In this...

Read more »

How do you know if your model is going to work? Part 4: Cross-validation techniques

September 21, 2015
By
How do you know if your model is going to work? Part 4: Cross-validation techniques

Authors: John Mount (more articles) and Nina Zumel (more articles). In this article we conclude our four part series on basic model testing. When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it’s better than the models that … Continue reading...

Read more »

Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

September 15, 2015
By
Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the model....

Read more »

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)