Trait SupervisedEstimator

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pub trait SupervisedEstimator<X, Y, P>: Predictor<X, Y> {
    // Required methods
    fn new() -> Self;
    fn fit(x: &X, y: &Y, parameters: P) -> Result<Self, Failed>
       where Self: Sized,
             P: Clone;
}
Expand description

An estimator for supervised learning, that provides method fit to learn from data and training values

Required Methods§

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fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

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fn fit(x: &X, y: &Y, parameters: P) -> Result<Self, Failed>
where Self: Sized, P: Clone,

Fit a model to a training dataset, estimate model’s parameters.

  • x - NxM matrix with N observations and M features in each observation.
  • y - target training values of size N.
  • parameters - hyperparameters of an algorithm

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§

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impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> SupervisedEstimator<X, Y, CategoricalNBParameters> for CategoricalNB<T, X, Y>

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impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, LogisticRegressionParameters<TX>> for LogisticRegression<TX, TY, X, Y>

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impl<TX: Number + FloatNumber + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, RandomForestClassifierParameters> for RandomForestClassifier<TX, TY, X, Y>

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impl<TX: Number + FloatNumber + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, RandomForestRegressorParameters> for RandomForestRegressor<TX, TY, X, Y>

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impl<TX: Number + RealNumber, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, GaussianNBParameters> for GaussianNB<TX, TY, X, Y>

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impl<TX: Number + RealNumber, TY: Number, X: Array2<TX> + CholeskyDecomposable<TX> + SVDDecomposable<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, RidgeRegressionParameters<TX>> for RidgeRegression<TX, TY, X, Y>

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impl<TX: Number + RealNumber, TY: Number, X: Array2<TX> + QRDecomposable<TX> + SVDDecomposable<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, LinearRegressionParameters> for LinearRegression<TX, TY, X, Y>

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impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, BernoulliNBParameters<TX>> for BernoulliNB<TX, TY, X, Y>

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impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, DecisionTreeClassifierParameters> for DecisionTreeClassifier<TX, TY, X, Y>

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impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, DecisionTreeRegressorParameters> for DecisionTreeRegressor<TX, TY, X, Y>

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impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, MultinomialNBParameters> for MultinomialNB<TX, TY, X, Y>

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impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> SupervisedEstimator<X, Y, KNNClassifierParameters<TX, D>> for KNNClassifier<TX, TY, X, Y, D>

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impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>> SupervisedEstimator<X, Y, KNNRegressorParameters<TX, D>> for KNNRegressor<TX, TY, X, Y, D>

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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, ElasticNetParameters> for ElasticNet<TX, TY, X, Y>

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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, LassoParameters> for Lasso<TX, TY, X, Y>