Algorithm for MICAP-NPSVM Customer Churn Prediction Framework Input: Customer churn training dataset D = {,C}, Ω = (f1, f2, ... , fn). The missing ratio vector MissingRatio for all the features. Parameters c1, c2, c3, c4, λ, μ, Output: Customer churn prediction function F(x).
Algorithm for MICAP-NPSVM Customer Churn Prediction Framework Input: Customer churn training dataset D = {,C}, Ω = (f1, f2, ... , fn). The missing ratio vector MissingRatio for all the features. Parameters c1, c2, c3, c4, λ, μ, Output: Customer churn prediction function F(x).
Operations Research : Applications and Algorithms
4th Edition
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Wayne L. Winston
Chapter20: Queuing Theory
Section20.4: The M/m/1/gd/∞/∞ Queuing System And The Queuing Formula L = Λw
Problem 14P
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Input:
Customer churn training dataset D = {,C}, Ω = (f1, f2, ... , fn). The
missing ratio vector
MissingRatio for all the features. Parameters c1, c2, c3, c4, λ, μ,
Output:
Customer churn prediction function F(x).
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