rust_ml::model::core::classification_model

Trait ClassificationModel

Source 
pub trait ClassificationModel<Input, Output>: OptimizableModel<Input, Output> {
    // Required methods
    fn accuracy(&self, x: &Input, y: &Output) -> Result<f64, ModelError>;
    fn loss(&self, x: &Input, y: &Output) -> Result<f64, ModelError>;
    fn recall(&self, x: &Input, y: &Output) -> Result<f64, ModelError>;
    fn f1_score(&self, x: &Input, y: &Output) -> Result<f64, ModelError>;
    fn compute_metrics(
        &self,
        x: &Input,
        y: &Output,
    ) -> Result<ClassificationMetrics, ModelError>;
}
Expand description

A trait for models that perform classification tasks.

This trait defines common evaluation metrics and functionality for classification models, allowing for standardized performance assessment across different implementations.

§Type Parameters

  • Input: The type of input data the model accepts
  • Output: The type of output data against which predictions are compared

Required Methods§

Source

fn accuracy(&self, x: &Input, y: &Output) -> Result<f64, ModelError>

Calculates the accuracy of the model on the given data.

Accuracy is defined as the proportion of correct predictions among the total number of predictions.

§Arguments
  • x - The input data
  • y - The expected output/ground truth
§Returns
  • Result<f64, ModelError> - The calculated accuracy score or an error
Source

fn loss(&self, x: &Input, y: &Output) -> Result<f64, ModelError>

Calculates the loss of the model on the given data.

The specific loss function depends on the model implementation.

§Arguments
  • x - The input data
  • y - The expected output/ground truth
§Returns
  • Result<f64, ModelError> - The calculated loss value or an error
Source

fn recall(&self, x: &Input, y: &Output) -> Result<f64, ModelError>

Calculates the recall score of the model on the given data.

Recall (also known as sensitivity) measures the proportion of actual positives that were correctly identified by the model.

§Arguments
  • x - The input data
  • y - The expected output/ground truth
§Returns
  • Result<f64, ModelError> - The calculated recall score or an error
Source

fn f1_score(&self, x: &Input, y: &Output) -> Result<f64, ModelError>

Calculates the F1 score of the model on the given data.

F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics.

§Arguments
  • x - The input data
  • y - The expected output/ground truth
§Returns
  • Result<f64, ModelError> - The calculated F1 score or an error
Source

fn compute_metrics( &self, x: &Input, y: &Output, ) -> Result<ClassificationMetrics, ModelError>

Computes multiple evaluation metrics for the model on the given data.

This method allows for efficient calculation of multiple metrics in a single pass through the data, potentially optimizing performance when multiple metrics are needed.

§Arguments
  • x - The input data
  • y - The expected output/ground truth
§Returns
  • Result<ModelParams, ModelError> - A collection of calculated metrics or an error

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§

Source§

impl ClassificationModel<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for LogisticRegression

Implementation of ClassificationModel trait for LogisticRegression