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R-squared for Decision Tree

@machinelearnbot

I use the methodology you speak of all the time. I was the original programer for Breiman and Stone's version of CART in the late 70's which is where I believe I was first introduced to that method. However we were very careful to use the term variation explained since there is little relationship to the theoretical Pearson "r". Be aware that this value can go negative. Which implies that parts of your model behave a lot higher variation then the population variance.


Chen

AAAI Conferences

In this demo we present a visualization of formalized representations of story. Introducing the interactive to storytelling requires the management of experiences that a user creates by their decisions. These sorts of variations can have impact on not only the user, but also the retrievable content appropriate to present to the user. The overall contribution of this work is to identify the player impact of story variation by modeling supplementary variations, and systematically responding to player interaction through supplementary variation, while respecting the author's intentions by maintaining the integrity of the core story skeleton.


Prediction of multi-dimensional spatial variation data via Bayesian tensor completion

arXiv.org Machine Learning

This paper presents a multi-dimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor computation. A tensor is a high-dimensional generalization of a matrix or a vector. By exploiting the hidden low-rank property of a high-dimensional data array, the large amount of unknown variation testing data may be predicted from a few random measurement samples. The tensor rank, which decides the complexity of a tensor representation, is decided by an available variational Bayesian approach. Our approach is validated by a practical chip testing data set, and it can be easily generalized to characterize the process variations of multiple wafers. Our approach is more efficient than the previous virtual probe techniques in terms of memory and computational cost when handling high-dimensional chip testing data.


Freshman or Fresher? Quantifying the Geographic Variation of Language in Online Social Media

AAAI Conferences

In this paper we present a new computational technique to detect and analyze statistically significant geographic variation in language. While previous approaches have primarily focused on lexical variation between regions, our method identifies words that demonstrate semantic and syntactic variation as well. We extend recently developed techniques for neural language models to learn word representations which capture differing semantics across geographical regions. In order to quantify this variation and ensure robust detection of true regional differences, we formulate a null model to determine whether observed changes are statistically significant. Our method is the first such approach to explicitly account for random variation due to chance while detecting regional variation in word meaning. To validate our model, we study and analyze two different massive online data sets: millions of tweets from Twitter as well as millions of phrases contained in the Google Book Ngrams. Our analysis reveals interesting facets of language change across countries.


[Report] Precipitation drives global variation in natural selection

Science

Climate change has the potential to affect the ecology and evolution of every species on Earth. Although the ecological consequences of climate change are increasingly well documented, the effects of climate on the key evolutionary process driving adaptation--natural selection--are largely unknown. We report that aspects of precipitation and potential evapotranspiration, along with the North Atlantic Oscillation, predicted variation in selection across plant and animal populations throughout many terrestrial biomes, whereas temperature explained little variation. By showing that selection was influenced by climate variation, our results indicate that climate change may cause widespread alterations in selection regimes, potentially shifting evolutionary trajectories at a global scale.