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Improving forecast of binary rare events data: A GAM-based approach
Raffaella Calabrese
, Silvia Osmetti
Business School
Management Science and Business Economics
Credit Research Centre
Management Science
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Earth and Planetary Sciences
Datum
100%
Model
100%
Class
50%
Regression
37%
Estimate
25%
Mathematical Model
25%
Size
25%
Logistics
25%
Probability Theory
25%
Medium
25%
Value
25%
Algorithms
12%
Quantile
12%
Proposal
12%
Data Set
12%
Mathematics
Rare Event
100%
Generalized Additive Model
100%
Regression Model
62%
Extreme Value
37%
Classes
37%
Linear Models
12%
Probability Theory
12%
Algorithm
12%
Predictive Accuracy
12%
Data Set
12%
Modeling
12%
Functions
12%
Additive Model
12%
Default Probability
12%
Quantile Function
12%
Logistic Regression Model
12%
Extreme Value Distribution
12%
Computer Science
Events
100%
Models
100%
Small and Medium Enterprise
25%
Probability
25%
Functions
25%
Classes
25%
Predictive Accuracy
12%
Minority Class
12%
Logistic Regression Model
12%
Links
12%
Regression
12%
Scoring Algorithm
12%
Additive Regression
12%
Modified Version
12%
Social Sciences
Class Size
25%
Enterprises
25%
Probability
25%
Logistics
25%
Algorithms
12%
Distribution
12%