326 search results for "evaluation"

Twinkle,twinkle little STAR

May 26, 2014
By
Twinkle,twinkle little STAR

At the recent R/Finance 2014 conference in Chicago I gave a talk on Smooth Transition AR models and a new package for estimating them called twinkle. In this blog post I will provide a short outline of the models and an introduction to the package and its features. Financial markets have a strong cyclical component

Read more »

The Purchase Funnel Survives the Consumer Decision Journey

May 19, 2014
By
The Purchase Funnel Survives the Consumer Decision Journey

The journey metaphor is almost irresistible. All one needs is a starting point and a finish line, plus some notion of progression. Thus, life is a journey, and so is love. Why not apply the metaphor to your next purchase? McKinsey & Company takes s...

Read more »

R has some sharp corners

May 15, 2014
By
R has some sharp corners

R is definitely our first choice go-to analysis system. In our opinion you really shouldn’t use something else until you have an articulated reason (be it a need for larger data scale, different programming language, better data source integration, or something else). The advantages of R are numerous: Single integrated work environment. Powerful unified scripting/programming Related posts:

Read more »

CFP: AusDM 2014 – the 12th Australasian Data Mining Conference

May 13, 2014
By
CFP: AusDM 2014 – the 12th Australasian Data Mining Conference

********************************************************* 12th Australasian Data Mining Conference (AusDM 2014) Brisbane, Australia 27-28 November 2014 http://ausdm14.ausdm.org/ ********************************************************* Data Mining is the art and science of intelligent analysis of (usually big) data sets for meaningful insights. Data mining is actively applied across all … Continue reading →

Read more »

Beyond R, or on the Hunt for New Tools

May 12, 2014
By

For more than four years now (judging by the first post on my old blog), R has been my primary tool for market research. It has thought me a lot, and it has helped me to me advance smoothly in the field of semi-automated trading. Lately however I started realizing that R is lacking essential

Read more »

Customer Satisfaction and Loyalty: Structural Equation Model or One-Dimensional Dissonance

May 9, 2014
By
Customer Satisfaction and Loyalty: Structural Equation Model or One-Dimensional Dissonance

Causal thinking is seductive. Product experience comes first, then feelings of satisfaction, and finally intentions to continue as a customer. Although customer satisfaction and loyalty data tend to be collected all at one time within the same question...

Read more »

“The Winner Takes It All” – Tuning and Validating R Recommendation Models Inside Tableau

May 4, 2014
By
“The Winner Takes It All” – Tuning and Validating R Recommendation Models Inside Tableau

Introduction My last blog article shows how to build an interactive recommendation engine in Tableau using a simple model utilizing the cosine similarity measure. While this can be a good way to explore unknown data, it is wise to validate any model before...

Read more »

Evaluating model performance – A practical example of the effects of overfitting and data size on prediction

May 3, 2014
By
Evaluating model performance – A practical example of the effects of overfitting and data size on prediction

Following my last post on decision making trees and machine learning, where I presented some tips gathered from the "Pragmatic Programming Techniques" blog, I have again been impressed by its clear presentation of strategies regarding the evaluation of model performance. I have seen some of these topics presented elsewhere -...

Read more »

Test coverage of the 10 most downloaded R packages

May 2, 2014
By
Test coverage of the 10 most downloaded R packages

Test coverage of the 10 most downloaded R packages 2014-04-30 Source Introduction How do you know that your code is well tested ? The test coverage is the proportion of source code lines that are executed (covered) when running the tests. It is useful to find the parts of your code that are no exercised no matter how...

Read more »

A bit of the agenda of Practical Data Science with R

May 1, 2014
By
A bit of the agenda of Practical Data Science with R

The goal of Zumel/Mount: Practical Data Science with R is to teach, through guided practice, the skills of a data scientist. We define a data scientist as the person who organizes client input, data, infrastructure, statistics, mathematics and machine learning to deploy useful predictive models into production. Our plan to teach is to: Order the Related posts:

Read more »