Module Interfaces.Sigs.Eval.Stats

module Stats: sig .. end

Statistics derived from trained models

type t = {

`n_samples :int;`	`(*`	Number of samples used for training	`*)`
`target_variance :float;`	`(*`	Variance of targets	`*)`
`sse :float;`	`(*`	Sum of squared errors	`*)`
`mse :float;`	`(*`	Mean sum of squared errors	`*)`
`rmse :float;`	`(*`	Root mean sum of squared errors	`*)`
`smse :float;`	`(*`	Standardized mean squared error	`*)`
`msll :float;`	`(*`	Mean standardized log loss	`*)`
`mad :float;`	`(*`	Mean absolute deviation	`*)`
`maxad :float;`	`(*`	Maximum absolute deviation	`*)`

}

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.