Kolmogorov-Arnold model
(for combination of quantized-continuous inputs)
Demo project code and dataThe dataset BANKCHURN.CSV is taken from Neural Designer web site. It contains 10,000 records of human decisions in terms of YES/NO to the bank offers. The input is combination of quantized and continuous parameters, shown below in a table:
| Name | Range | Type |
| Credit score | 350-850 | continuous |
| Country | 0-2 | enumeration |
| Gender | 0-1 | enumeration |
| Age | 18-92 | continuous |
| Tenure | 0-10 | continuous |
| Balance | 0-250898.09 | continuous |
| Product | 0-3 | enumeration |
| Credit card | 0-1 | enumeration |
| Is active | 0-1 | enumeration |
| Salary | 11.58-199992.48 | continuous |
The functions $\phi_{q,p}$ of Kolmogorov-Arnold model were chosen according to input type either quantized or continuous.
$$ f(x_1, x_2, x_3, ... , x_n) = \sum_{q=0}^{2n} \Phi_q\left(\sum_{p=1}^{n} \phi_{q,p}(x_{p})\right), $$
The accuracy was measured by 10 fold cross-validation. About 80% of all predictions were correct. It is slightly above the accuracy reported by Neural Designer (78.9%).
