Loss functions¶
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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
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statinf.ml.losses.binary_cross_entropy(y_true, y_pred, tensor=False)[source]¶ Binary cross-entropy
- Parameters
y_true (numpy.array) – Real values on which to compare.
y_pred (numpy.array) – Predicted values.
tensor (bool, optional) – Perform computation as tensor (theano type), defaults to False
- 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
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statinf.ml.losses.log_stability(x)[source]¶ Log-stability for computing loss
- Parameters
x (float) – Input value
- Returns
Scaled value where \(\hat{x} \in (0, 1)\)
- Return type
float
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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