module Stats: sig
.. end
Statistics derived from trained models
type
t = {
|
n_samples : int ; |
|
target_variance : float ; |
|
sse : float ; |
|
mse : float ; |
|
rmse : float ; |
|
smse : float ; |
|
msll : float ; |
|
mad : float ; |
|
maxad : float ; |
}
Type of full statistics
val calc_n_samples : Interfaces.Sigs.Eval.Trained.t -> int
calc_n_samples trained
Returns number of samples used for training
trained
.
val calc_target_variance : Interfaces.Sigs.Eval.Trained.t -> float
calc_target_variance trained
Returns variance of targets used for
training trained
.
val calc_sse : Interfaces.Sigs.Eval.Trained.t -> float
calc_sse trained
Returns the sum of squared errors of the trained
model.
val calc_mse : Interfaces.Sigs.Eval.Trained.t -> float
calc_mse trained
Returns the mean sum of squared errors of the
trained
model.
val calc_rmse : Interfaces.Sigs.Eval.Trained.t -> float
calc_sse trained
Returns the root of the mean sum of squared errors
of the trained
model.
val calc_smse : Interfaces.Sigs.Eval.Trained.t -> float
calc_smse trained
Returns the standardized mean squared error of the
trained
model. This is equivalent to the mean squared error divided
by the target variance.
val calc_msll : Interfaces.Sigs.Eval.Trained.t -> float
calc_msll trained
Returns the mean standardized log loss. This
is equivalent to subtracting the log evidence of the trained model
from the log evidence of a normal distribution fit to the targets, and
dividing the result by the number of samples.
val calc_mad : Interfaces.Sigs.Eval.Trained.t -> float
calc_mad trained
Returns the mean absolute deviation
of the trained
model.
val calc_maxad : Interfaces.Sigs.Eval.Trained.t -> float
calc_mad trained
Returns the maximum absolute deviation
of the trained
model.
val calc : Interfaces.Sigs.Eval.Trained.t -> t
calc trained
Returns the full set of statistics associated with
the trained
model.