Struct LogisticRegressionParameters 

pub struct LogisticRegressionParameters<T>
where
    T: RealNumber,
{
    pub solver: LogisticRegressionSolverName,
    pub alpha: T,
}

Parameters for logistic regression (re-export from Smartcore) Logistic Regression parameters

Fields

solver: LogisticRegressionSolverName

Solver to use for estimation of regression coefficients.

alpha: T

Regularization parameter.

Implementations

impl<T> LogisticRegressionParameters<T>

where T: RealNumber,

pub fn with_solver( self, solver: LogisticRegressionSolverName, ) -> LogisticRegressionParameters<T>

Solver to use for estimation of regression coefficients.

Examples found in repository

examples/maximal_classification.rs (line 31)

fn main() {
    // Totally customize settings
    let settings = Settings::default_classification()
        .with_number_of_folds(3)
        .shuffle_data(true)
        .verbose(true)
        .with_final_model(FinalModel::Blending {
            algorithm: Algorithm::CategoricalNaiveBayes,
            meta_training_fraction: 0.15,
            meta_testing_fraction: 0.15,
        })
        .skip(Algorithm::RandomForestClassifier)
        .sorted_by(Metric::Accuracy)
        .with_preprocessing(PreProcessing::ReplaceWithPCA {
            number_of_components: 5,
        })
        .with_random_forest_classifier_settings(
            RandomForestClassifierParameters::default()
                .with_m(100)
                .with_max_depth(5)
                .with_min_samples_leaf(20)
                .with_n_trees(100)
                .with_min_samples_split(20),
        )
        .with_logistic_settings(
            LogisticRegressionParameters::default()
                .with_alpha(1.0)
                .with_solver(LogisticRegressionSolverName::LBFGS),
        )
        .with_svc_settings(
            SVCParameters::default()
                .with_epoch(10)
                .with_tol(1e-10)
                .with_c(1.0)
                .with_kernel(Kernel::Linear),
        )
        .with_decision_tree_classifier_settings(
            DecisionTreeClassifierParameters::default()
                .with_min_samples_split(20)
                .with_max_depth(5)
                .with_min_samples_leaf(20),
        )
        .with_knn_classifier_settings(
            KNNClassifierParameters::default()
                .with_algorithm(KNNAlgorithmName::CoverTree)
                .with_k(3)
                .with_distance(Distance::Euclidean)
                .with_weight(KNNWeightFunction::Uniform),
        )
        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));

    // Save the settings for later use
    settings.save("examples/maximal_classification_settings.yaml");

    // Load a dataset from smartcore and add it to the regressor
    let mut model =
        SupervisedModel::new(smartcore::dataset::breast_cancer::load_dataset(), settings);

    // Run a model comparison with all models at default settings
    model.train();

    // Print the results
    println!("{}", model);

    // Save teh model for later
    model.save("examples/maximal_classification_model.aml");
}

pub fn with_alpha(self, alpha: T) -> LogisticRegressionParameters<T>

Regularization parameter.

Examples found in repository

examples/maximal_classification.rs (line 30)

fn main() {
    // Totally customize settings
    let settings = Settings::default_classification()
        .with_number_of_folds(3)
        .shuffle_data(true)
        .verbose(true)
        .with_final_model(FinalModel::Blending {
            algorithm: Algorithm::CategoricalNaiveBayes,
            meta_training_fraction: 0.15,
            meta_testing_fraction: 0.15,
        })
        .skip(Algorithm::RandomForestClassifier)
        .sorted_by(Metric::Accuracy)
        .with_preprocessing(PreProcessing::ReplaceWithPCA {
            number_of_components: 5,
        })
        .with_random_forest_classifier_settings(
            RandomForestClassifierParameters::default()
                .with_m(100)
                .with_max_depth(5)
                .with_min_samples_leaf(20)
                .with_n_trees(100)
                .with_min_samples_split(20),
        )
        .with_logistic_settings(
            LogisticRegressionParameters::default()
                .with_alpha(1.0)
                .with_solver(LogisticRegressionSolverName::LBFGS),
        )
        .with_svc_settings(
            SVCParameters::default()
                .with_epoch(10)
                .with_tol(1e-10)
                .with_c(1.0)
                .with_kernel(Kernel::Linear),
        )
        .with_decision_tree_classifier_settings(
            DecisionTreeClassifierParameters::default()
                .with_min_samples_split(20)
                .with_max_depth(5)
                .with_min_samples_leaf(20),
        )
        .with_knn_classifier_settings(
            KNNClassifierParameters::default()
                .with_algorithm(KNNAlgorithmName::CoverTree)
                .with_k(3)
                .with_distance(Distance::Euclidean)
                .with_weight(KNNWeightFunction::Uniform),
        )
        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));

    // Save the settings for later use
    settings.save("examples/maximal_classification_settings.yaml");

    // Load a dataset from smartcore and add it to the regressor
    let mut model =
        SupervisedModel::new(smartcore::dataset::breast_cancer::load_dataset(), settings);

    // Run a model comparison with all models at default settings
    model.train();

    // Print the results
    println!("{}", model);

    // Save teh model for later
    model.save("examples/maximal_classification_model.aml");
}

Trait Implementations

impl<T> Clone for LogisticRegressionParameters<T>

where T: Clone + RealNumber,

fn clone(&self) -> LogisticRegressionParameters<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T> Debug for LogisticRegressionParameters<T>

where T: Debug + RealNumber,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<T> Default for LogisticRegressionParameters<T>

where T: RealNumber,

fn default() -> LogisticRegressionParameters<T>

Returns the “default value” for a type.

impl<'de, T> Deserialize<'de> for LogisticRegressionParameters<T>

where T: RealNumber + Deserialize<'de>,

fn deserialize<__D>( __deserializer: __D, ) -> Result<LogisticRegressionParameters<T>, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<T> Serialize for LogisticRegressionParameters<T>

where T: RealNumber + Serialize,

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.