boa.metrics.metrics.METRICS#

class boa.metrics.metrics.METRICS[source]#

Bases: object

Methods

`MSE`	Mean squared error regression loss.
`Mean`	Arithmetic mean along the specified axis for your metric, Defaults to minimizization, if you want to maximize, specify lower_is_better: False or minimize: False in your configuration
`MeanSquaredError`	Mean squared error regression loss.
`NRMSE`	Normalized root mean squared error.
`NormalizedRootMeanSquaredError`	Normalized root mean squared error.
`R2`	\(R^2\) (coefficient of determination) regression score function.
`RMSE`	Root mean squared error regression loss.
`RSquared`	\(R^2\) (coefficient of determination) regression score function.
`RootMeanSquaredError`	Root mean squared error regression loss.
`mean_squared_error`	Mean squared error regression loss.
`normalized_root_mean_squared_error`	Normalized root mean squared error.
`root_mean_squared_error`	Root mean squared error regression loss.

MSE()#: Mean squared error regression loss. Read more from sklearn mean squared error

Mean()#: Arithmetic mean along the specified axis for your metric, Defaults to minimizization, if you want to maximize, specify lower_is_better: False or minimize: False in your configuration

MeanSquaredError()#: Mean squared error regression loss. Read more from sklearn mean squared error

NRMSE()#: Normalized root mean squared error. Like a normalized version of RMSE. Normalization defaults to IQR (inner quartile range).

NormalizedRootMeanSquaredError()#: Normalized root mean squared error. Like a normalized version of RMSE. Normalization defaults to IQR (inner quartile range).

R2()#

\(R^2\) (coefficient of determination) regression score function.

Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). In the general case when the true y is non-constant, a constant model that always predicts the average y disregarding the input features would get a \(R^2\) score of 0.0.

RMSE()#: Root mean squared error regression loss. Read more from sklearn mean squared error with squared=False

RSquared()#

\(R^2\) (coefficient of determination) regression score function.

RootMeanSquaredError()#: Root mean squared error regression loss. Read more from sklearn mean squared error with squared=False

mean_squared_error()#: Mean squared error regression loss. Read more from sklearn mean squared error

normalized_root_mean_squared_error()#: Normalized root mean squared error. Like a normalized version of RMSE. Normalization defaults to IQR (inner quartile range).

root_mean_squared_error()#: Root mean squared error regression loss. Read more from sklearn mean squared error with squared=False