calcNCE: Calculate the normalized cross entropy

In logicDT: Identifying Interactions Between Binary Predictors

Calculate the normalized cross entropy

Description

This function computes the normalized cross entropy (NCE) which is given by

\mathrm{NCE} = \frac{\frac{1}{N} \sum_{i=1}^{N} y_i \cdot \log(p_i) + (1-y_i) \cdot \log(1-p_i)}{ p \cdot \log(p) + (1-p) \cdot \log(1-p)}

where (for i \in \lbrace 1,\ldots,N \rbrace) y_i \in \lbrace 0,1 \rbrace are the true classes, p_i are the risk/probability predictions and p = \frac{1}{N} \sum_{i=1}^{N} y_i is total unrestricted empirical risk estimate.

Usage

calcNCE(preds, y)

Arguments

preds	Numeric vector of risk estimates
y	Vector of true binary outcomes

Details

Smaller values towards zero are generally prefered. A NCE of one or above would indicate that the used model yields comparable or worse predictions than the naive mean model.

Value

The normalized cross entropy

References

• He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., Candela, J. Q. (2014). Practical Lessons from Predicting Clicks on Ads at Facebook. Proceedings of the Eighth International Workshop on Data Mining for Online Advertising 1-9. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1145/2648584.2648589")}

logicDT documentation built on Sept. 23, 2024, 5:11 p.m.