Loss functions
- statinf.ml.losses.binary_accuracy(y_true, y_pred)[source]
Accuracy for binary data.
- Parameters
y_true (
numpy.array) – Real values on which to compare.y_pred (numpy.array) – Predicted values
- Returns
Binary accuracy (in percent)
- Return type
float
- statinf.ml.losses.binary_cross_entropy(y_true, y_pred)[source]
Binary cross-entropy.
- Parameters
y_true (
numpy.array) – Real values on which to compare.y_pred (
numpy.array) – Predicted values.
- Formula
\(loss = y_{i} \log \left[ \hat{y}_{i} \right] + (1 - y_{i}) \log \left[1 - \hat{y}_{i} \right]\)
- References
Friedman, J., Hastie, T. and Tibshirani, R., 2001. The elements of statistical learning. Ch. 2, pp. 24.
- Returns
Binary cross-entropy
- Return type
float
- statinf.ml.losses.log_stability(x, delta=0.0001)[source]
Log-stability for computing loss.
- Parameters
x (
float) – Input value.delta (
float, optional) – Constant to move from 0 or 1, defaults to 10e-9.
- Returns
Stabilized value where \(\hat{x} \in (0, 1)\)
- Return type
float
- statinf.ml.losses.mean_squared_error(y_true, y_pred, root=False)[source]
Mean Squared Error.
- Parameters
y_true (
numpy.array) – Real values on which to compare.y_pred (
numpy.array) – Predicted values.root (
bool, optional) – Return Root Mean Squared Error (RMSE), defaults to False.
- Formula
\(loss = \dfrac{1}{m} \times \sum_{i=1}^{m} (y_i - \hat{y}_i)^2\)
- References
Friedman, J., Hastie, T. and Tibshirani, R., 2001. The elements of statistical learning. Ch. 2, pp. 24.
- Returns
Mean Squared Error or its root.
- Return type
float