isanet.metrics¶
Metrics Module.
-
isanet.metrics.accuracy_binary(y_true, y_pred)¶ Accuracy classification score.
- Parameters
y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Ground truth (correct) target values.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Estimated target values.
- Returns
loss – A non-negative floating point value (the best value is 100.0)
- Return type
float
-
isanet.metrics.mee(y_true, y_pred)¶ Mean Euclidean error regression loss
- Parameters
y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Ground truth (correct) target values.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Estimated target values.
- Returns
loss – A non-negative floating point value (the best value is 0.0)
- Return type
float
-
isanet.metrics.mse(y_true, y_pred)¶ Mean squared error regression loss
- Parameters
y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Ground truth (correct) target values.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Estimated target values.
- Returns
loss – A non-negative floating point value (the best value is 0.0)
- Return type
float
-
isanet.metrics.mse_reg(y_true, y_pred, model, weights)¶ MSE + L2 regularization
- Parameters
y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Ground truth (correct) target values.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Estimated target values.
model (isanet.model.MLP) –
weights (list) – List of arrays, the ith array represents all the weights of each neuron in the ith layer.
- Returns
loss – A non-negative floating point value (the best value is 0.0)
- Return type
float