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Classification Techniques
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Publisher: Editura Universitară
Author: Monica Mihaela Maer Matei,, Ionela Cătălina Zamfir, Andreea Muraru
Edition: I
Pages: 102
Publisher year: 2024
ISBN: 978-606-28-1815-9
DOI: 10.5682/9786062818159
Product Code:
9786062818159
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Making informed decisions regarding investments, production and business strategies is based on the results provided by the analysis of the available data. Frequently, the use of classification techniques contributes to the optimization of decision-making processes. Their concrete applications in the economic field refer to the detection of financial fraud, customer segmentation, staff recruitment, credit granting decisions, development of trading strategies or customer retention.
The chapters of the book Classification Techniques present the theoretical concepts needed by analysts for the correct use of techniques such as the Bayesian classifier, multinomial logistic regression and the random forest classifier. These are illustrated with the help of concrete examples and the facilities offered by the R programming environment.
The chapters of the book Classification Techniques present the theoretical concepts needed by analysts for the correct use of techniques such as the Bayesian classifier, multinomial logistic regression and the random forest classifier. These are illustrated with the help of concrete examples and the facilities offered by the R programming environment.
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Classification techniques
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MONICA MIHAELA MAER MATEI
IONELA CATALINA ZAMFIR
ANDREEA MURARU
IONELA CATALINA ZAMFIR
ANDREEA MURARU
Notions specific to the performance evaluation of a classifier / 9
Stochastic classifiers / 13
Theoretical aspects / 13
Solved applications / 18
References / 44
Multinomial logistic regression / 46
General considerations / 46
Logistic regression – associated terminology and interpretation / 47
Estimation and validation of the model / 53
Tests for the creditworthiness of the model / 55
Cumulative multinomial logit for dependent variables of ordinal type / 56
Other logit models / 58
Evaluation of predictive capacity / 71
References / 74
Random forest / 76
Necessary concepts / 76
Specific concepts "random forest" / 94
References / 100
Stochastic classifiers / 13
Theoretical aspects / 13
Solved applications / 18
References / 44
Multinomial logistic regression / 46
General considerations / 46
Logistic regression – associated terminology and interpretation / 47
Estimation and validation of the model / 53
Tests for the creditworthiness of the model / 55
Cumulative multinomial logit for dependent variables of ordinal type / 56
Other logit models / 58
Evaluation of predictive capacity / 71
References / 74
Random forest / 76
Necessary concepts / 76
Specific concepts "random forest" / 94
References / 100
Making informed decisions regarding investments, production and business strategies is based on the results provided by the analysis of the available data. Frequently, the use of classification techniques contributes to the optimization of decision-making processes. Their concrete applications in the economic field refer to the detection of financial fraud, customer segmentation, staff recruitment, credit granting decisions, development of trading strategies or customer retention.
The chapters of the book Classification Techniques present the theoretical concepts needed by analysts for the correct use of techniques such as the Bayesian classifier, multinomial logistic regression and the random forest classifier. These are illustrated with the help of concrete examples and the facilities offered by the R programming environment.
The chapters of the book Classification Techniques present the theoretical concepts needed by analysts for the correct use of techniques such as the Bayesian classifier, multinomial logistic regression and the random forest classifier. These are illustrated with the help of concrete examples and the facilities offered by the R programming environment.
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