metrics.logloss.solve: Logarithmic Loss Solver
In Laurae2/LauraeDS: Laurae's Data Science Package

Description Usage Arguments Value Examples

Reverse engineers the prediction or the positive sample ratio to provide to achieve a known loss.

1 2	metrics.logloss.solve(to_solve, known_loss = NULL, known_pred = NULL, known_ratio = NULL)

`to_solve`	Type: character. What to solve. `"pred"` solves for the best constant prediction value provided the `known_loss` and `known_ratio`. Use `known_loss` as `0` if you want to minimize the loss function. Provide another value when reverse-engineering the prediction is required. Although the solving equation can be expressed as an exact formula (with an exact solution), the solver uses an approximate method (Brent) to fit rare cases such as loss minimization provided a single ratio. `"ratio"` solves for the best constant ratio of positives over (positives + negatives), provided the `known_loss` and `known_pred`. Use `known_loss` as `0` if you want to minimize the loss function. Provide another value when reverse-engineering the ratio is required. It uses an approximate solving method (Brent).
`known_loss`	Type: numeric. The known loss issued from the logartihmic loss.
`known_pred`	Type: numeric. The prediction value which must be fixed. Must be provided when `to_solve == "ratio"` Defaults to `NULL`.
`known_ratio`	Type: numeric. The positive ratio which must be fixed. Must be provided when `to_solve == "pred"` Defaults to `NULL`.

The solved value.

# Note: this example unexpectedly fails when using pkgdown.
# Example from https://www.kaggle.com/opanichev/mean-baseline-lb-0-30786/code
# WSDM - KKBox's Churn Prediction Challenge (public score: 0.17689)

# Reverse engineeer ratio of positives in Public Leaderboard
print(metrics.logloss.solve(to_solve = "ratio",
                            known_loss = 0.17695680071494552,
                            known_pred = 0.08994191315811156), digits = 17)

# Reverse engineer the prediction value used in Public Leaderboard
print(metrics.logloss.solve(to_solve = "pred",
                            known_loss = 0.17695680071494552,
                            known_ratio = 29650 / (800000 + 29650)), digits = 17)

# Find better prediction value for the Public Leaderboard
print(metrics.logloss.solve(to_solve = "pred",
                            known_loss = 0,
                            known_ratio = 29650 / (800000 + 29650)), digits = 17)
cat("My better logloss: ",
    -1 * ((0.03573796) * log(0.03573796) + ((1 - 0.03573796) * log(1 - 0.03573796))),
    sep = "")