Struct RegressionProfiler 

pub struct RegressionProfiler<Model, Opt, Input, Output> { /* private fields */ }

A profiler for regression models that measures training time and computes regression metrics.

This struct implements the Profiler trait specifically for regression models, providing performance assessment through metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R²).

Type Parameters

• Model - The regression model type being profiled
• Opt - The optimizer type used for training
• Input - The input data type (features)
• Output - The output data type (target values)

Implementations

impl<Model, Opt, Input, Output> RegressionProfiler<Model, Opt, Input, Output>

pub fn new() -> Self

Creates a new RegressionProfiler instance.

Returns

A new RegressionProfiler instance.

Trait Implementations

impl<Model, Opt, Input, Output> Default for RegressionProfiler<Model, Opt, Input, Output>

fn default() -> Self

Returns the “default value” for a type.

impl<Model, Opt, Input, Output> Profiler<Model, Opt, Input, Output> for RegressionProfiler<Model, Opt, Input, Output>

where Model: BaseModel<Input, Output> + RegressionModel<Input, Output>, Opt: Optimizer<Input, Output, Model>,

fn train( &self, model: &mut Model, optimizer: &mut Opt, x: &Input, y: &Output, ) -> Result<(TrainMetrics, Self::EvalMetrics), ProfilerError>

Profiles the training process of a regression model.

Measures the time taken to train the model and computes regression metrics on the provided data.

Arguments

• model - Mutable reference to the regression model being trained
• optimizer - Mutable reference to the optimizer used for training
• x - Reference to input features
• y - Reference to target values

Returns

A tuple containing training metrics (including training time) and regression metrics (MSE, RMSE, R²), or a ProfilerError if an error occurs.

fn profile_evaluation( &self, model: &mut Model, x: &Input, y: &Output, ) -> Result<Self::EvalMetrics, ProfilerError>

Profiles the evaluation process of a regression model.

Computes regression metrics for the model on the provided data.

Arguments

• model - Mutable reference to the regression model being evaluated
• x - Reference to input features
• y - Reference to target values

Returns

Regression metrics (MSE, RMSE, R²), or a ProfilerError if an error occurs.

type EvalMetrics = RegressionMetrics

The type of evaluation metrics returned by the profiler