| Interface | Description |
|---|---|
| LossFunction |
Interface implemented by loss functions for MLPRegressor and MLPClassifier.
|
| Class | Description |
|---|---|
| ApproximateAbsoluteError |
Approximate absolute error for MLPRegressor and MLPClassifier:
loss(a, b) = sqrt((a-b)^2+epsilon) Valid options are: |
| SquaredError |
Squared error for MLPRegressor and MLPClassifier:
loss(a, b) = (a-b)^2 |