Struct tangram_linear::BinaryClassifier

pub struct BinaryClassifier {
    pub bias: f32,
    pub weights: Array1<f32>,
    pub means: Vec<f32>,
}

This struct describes a linear binary classifier model. You can train one by calling BinaryClassifier::train.

Fields

bias: f32

This is the bias the model learned.

weights: Array1<f32>

These are the weights the model learned.

means: Vec<f32>

These are the mean values of each feature in the training set. They are used to compute SHAP values.

Implementations

impl BinaryClassifier

pub fn train(
    features: ArrayView2<'_, f32>,
    labels: EnumTableColumnView<'_>,
    train_options: &TrainOptions,
    progress: Progress<'_>
) -> BinaryClassifierTrainOutput

Train a linear binary classifier.

pub fn compute_loss(
    probabilities: ArrayView1<'_, f32>,
    labels: ArrayView1<'_, Option<NonZeroUsize>>
) -> f32

pub fn predict(
    &self,
    features: ArrayView2<'_, f32>,
    probabilities: ArrayViewMut1<'_, f32>
)

Write predicted probabilities into probabilities for the input features.

pub fn compute_feature_contributions(
    &self,
    features: ArrayView2<'_, f32>
) -> Vec<ComputeShapValuesForExampleOutput>

pub fn from_reader(
    binary_classifier: BinaryClassifierReader<'_>
) -> BinaryClassifier

pub fn to_writer(&self, writer: &mut Writer) -> Position<BinaryClassifierWriter>

pub fn from_bytes(&self, bytes: &[u8]) -> BinaryClassifier

pub fn to_bytes(&self) -> Vec<u8>

Trait Implementations

impl Clone for BinaryClassifier

fn clone(&self) -> BinaryClassifier

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for BinaryClassifier

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

Formats the value using the given formatter.