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Random Forests for multiclass classification: Random MultiNomial Logit · Study

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ArticleRandom Forests for multiclass classification: Random MultiNomial Logit

Anita A. Prinzie2007

Article2007

Random Forests for multiclass classification: Random MultiNomial Logit

Anita A. Prinzie · Dirk Van den Poel

193 citations·0/0 files stored·Expert Systems with Applications

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Customer churn and segmentation Statistical Methods and Inference Data Mining and Machine Learning Applications

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References

1987Discrete Choice Analysis: Theory and Application to Travel Demand.1988Discrete choice analysis: Theory and application to travel demand 1991Deriving Categories to Achieve Goals 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting 1983Discrete choice theory, information theory and the multinomial logit and gravity models 1984Consumer Choice Strategies for Comparing Noncomparable Alternatives

Cited by

2011Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics 2021A comparative study of automated legal text classification using random forests and deep learning 2018Online big data-driven oil consumption forecasting with Google trends 2020Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market 2011An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction 2012Customer event history for churn prediction: How long is long enough?

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Year2007

Typearticle

Published2007-02-07

VenueExpert Systems with Applications

ISSN-L0957-4174

Languageen

Citations193

Files0 public / 0

ShelfCustomer churn and segmentation

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doi10.1016/j.eswa.2007.01.029

openalex_workW1990960780

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2020

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