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Nova Credit Income Model Compute Confidence Score
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| /** | |
| * Computes the sum of the intercept and the dot product of the coefficients and features for logistic regression model | |
| * Using the formula s = b[0] + sum(b[1:] * x[1:]) | |
| * and then computes the confidence score from this sum using the formula p = 1 / (1 + exp(-s)) | |
| * @param normalizedFeatureValues the `x` vector, or the normalized model features | |
| * @param coefficients the `b` vector, or the coefficients | |
| * @param modelType conventional or non-conventional | |
| */ | |
| export const computeConfidenceScore = ({ | |
| normalizedFeatureValues, | |
| coefficients, | |
| modelType, | |
| }: { | |
| normalizedFeatureValues: number[]; | |
| coefficients: number[]; | |
| modelType: MODEL_TYPES; | |
| }): number => { | |
| const b0 = MODEL_FEATURE_TO_COEFFICIENT_MEAN_SCALE[modelType].intercept && MODEL_FEATURE_TO_COEFFICIENT_MEAN_SCALE[modelType].intercept.COEFFICIENT; | |
| if (!b0) { | |
| throw new commonErrors.NovaError(`Intercept coefficient not defined for model type ${modelType}`); | |
| } | |
| const sum = b0 + dot(coefficients, normalizedFeatureValues); | |
| const p = 1 / (1 + Math.exp(-sum)); | |
| return 100 * p; | |
| }; |
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