Knygos.lt klubas Knygos.lt nariams
151,96 €
-30%
Įprastai
217,09 €
Application of AI in Credit Scoring Modeling
Application of AI in Credit Scoring Modeling
Knygos.lt klubas Knygos.lt nariams
151,96 €
-30%
Įprastai
217,09 €
  • Išsiųsime per 12–18 d.d.
The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.
  • Leidėjas:
  • ISBN-10: 3658401796
  • ISBN-13: 9783658401795
  • Formatas: 14.8 x 21 x 0.6 cm, minkšti viršeliai
  • Kalba: Anglų

Application of AI in Credit Scoring Modeling (el. knyga) (skaityta knyga) | knygos.lt

Atsiliepimai

(5.00 Goodreads įvertinimas)

Aprašymas

The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.

Knygos.lt klubas
Knygos.lt nariams
151,96 €
-30%
Įprastai
217,09 €
Kaina registruotiems pirkėjams
Prisijunkite ir už šią prekę
gausite 2,17 Knygų Eurų!?
Išsiųsime per 12–18 d.d.
Įsigykite dovanų kuponą
Daugiau
  • Autorius: Bohdan Popovych
  • Leidėjas:
  • ISBN-10: 3658401796
  • ISBN-13: 9783658401795
  • Formatas: 14.8 x 21 x 0.6 cm, minkšti viršeliai
  • Kalba: Anglų

The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.

Atsiliepimai

  • Atsiliepimų nėra
0 pirkėjai įvertino šią prekę.
5
0%
4
0%
3
0%
2
0%
1
0%
(rodomas nebus)