automl::settings

Struct DecisionTreeClassifierParameters

Source 
pub struct DecisionTreeClassifierParameters {
    pub criterion: SplitCriterion,
    pub max_depth: Option<u16>,
    pub min_samples_leaf: usize,
    pub min_samples_split: usize,
}
Expand description

Parameters for decision tree classification (re-export from Smartcore) Parameters of Decision Tree

Fields§

§criterion: SplitCriterion

Split criteria to use when building a tree.

§max_depth: Option<u16>

The maximum depth of the tree.

§min_samples_leaf: usize

The minimum number of samples required to be at a leaf node.

§min_samples_split: usize

The minimum number of samples required to split an internal node.

Implementations§

Source§

impl DecisionTreeClassifierParameters

Source

pub fn with_criterion( self, criterion: SplitCriterion, ) -> DecisionTreeClassifierParameters

Split criteria to use when building a tree.

Source

pub fn with_max_depth(self, max_depth: u16) -> DecisionTreeClassifierParameters

The maximum depth of the tree.

Examples found in repository?
examples/maximal_classification.rs (line 43)
4fn main() {
5    // Totally customize settings
6    let settings = Settings::default_classification()
7        .with_number_of_folds(3)
8        .shuffle_data(true)
9        .verbose(true)
10        .with_final_model(FinalModel::Blending {
11            algorithm: Algorithm::CategoricalNaiveBayes,
12            meta_training_fraction: 0.15,
13            meta_testing_fraction: 0.15,
14        })
15        .skip(Algorithm::RandomForestClassifier)
16        .sorted_by(Metric::Accuracy)
17        .with_preprocessing(PreProcessing::ReplaceWithPCA {
18            number_of_components: 5,
19        })
20        .with_random_forest_classifier_settings(
21            RandomForestClassifierParameters::default()
22                .with_m(100)
23                .with_max_depth(5)
24                .with_min_samples_leaf(20)
25                .with_n_trees(100)
26                .with_min_samples_split(20),
27        )
28        .with_logistic_settings(
29            LogisticRegressionParameters::default()
30                .with_alpha(1.0)
31                .with_solver(LogisticRegressionSolverName::LBFGS),
32        )
33        .with_svc_settings(
34            SVCParameters::default()
35                .with_epoch(10)
36                .with_tol(1e-10)
37                .with_c(1.0)
38                .with_kernel(Kernel::Linear),
39        )
40        .with_decision_tree_classifier_settings(
41            DecisionTreeClassifierParameters::default()
42                .with_min_samples_split(20)
43                .with_max_depth(5)
44                .with_min_samples_leaf(20),
45        )
46        .with_knn_classifier_settings(
47            KNNClassifierParameters::default()
48                .with_algorithm(KNNAlgorithmName::CoverTree)
49                .with_k(3)
50                .with_distance(Distance::Euclidean)
51                .with_weight(KNNWeightFunction::Uniform),
52        )
53        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
54        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
55
56    // Save the settings for later use
57    settings.save("examples/maximal_classification_settings.yaml");
58
59    // Load a dataset from smartcore and add it to the regressor
60    let mut model =
61        SupervisedModel::new(smartcore::dataset::breast_cancer::load_dataset(), settings);
62
63    // Run a model comparison with all models at default settings
64    model.train();
65
66    // Print the results
67    println!("{}", model);
68
69    // Save teh model for later
70    model.save("examples/maximal_classification_model.aml");
71}
More examples
Hide additional examples
examples/print_settings.rs (line 88)
3fn main() {
4    let regressor_settings = automl::Settings::default_regression()
5        .with_number_of_folds(3)
6        .shuffle_data(true)
7        .verbose(true)
8        .sorted_by(Metric::RSquared)
9        .with_preprocessing(PreProcessing::AddInteractions)
10        .with_linear_settings(
11            LinearRegressionParameters::default().with_solver(LinearRegressionSolverName::QR),
12        )
13        .with_lasso_settings(
14            LassoParameters::default()
15                .with_alpha(10.0)
16                .with_tol(1e-10)
17                .with_normalize(true)
18                .with_max_iter(10_000),
19        )
20        .with_ridge_settings(
21            RidgeRegressionParameters::default()
22                .with_alpha(10.0)
23                .with_normalize(true)
24                .with_solver(RidgeRegressionSolverName::Cholesky),
25        )
26        .with_elastic_net_settings(
27            ElasticNetParameters::default()
28                .with_tol(1e-10)
29                .with_normalize(true)
30                .with_alpha(1.0)
31                .with_max_iter(10_000)
32                .with_l1_ratio(0.5),
33        )
34        .with_knn_regressor_settings(
35            KNNRegressorParameters::default()
36                .with_algorithm(KNNAlgorithmName::CoverTree)
37                .with_k(3)
38                .with_distance(Distance::Euclidean)
39                .with_weight(KNNWeightFunction::Uniform),
40        )
41        .with_svr_settings(
42            SVRParameters::default()
43                .with_eps(1e-10)
44                .with_tol(1e-10)
45                .with_c(1.0)
46                .with_kernel(Kernel::Linear),
47        )
48        .with_random_forest_regressor_settings(
49            RandomForestRegressorParameters::default()
50                .with_m(100)
51                .with_max_depth(5)
52                .with_min_samples_leaf(20)
53                .with_n_trees(100)
54                .with_min_samples_split(20),
55        )
56        .with_decision_tree_regressor_settings(
57            DecisionTreeRegressorParameters::default()
58                .with_min_samples_split(20)
59                .with_max_depth(5)
60                .with_min_samples_leaf(20),
61        );
62
63    let classifier_settings = automl::Settings::default_classification()
64        .with_number_of_folds(3)
65        .shuffle_data(true)
66        .verbose(true)
67        .sorted_by(Metric::Accuracy)
68        .with_preprocessing(PreProcessing::AddInteractions)
69        .with_random_forest_classifier_settings(
70            RandomForestClassifierParameters::default()
71                .with_m(100)
72                .with_max_depth(5)
73                .with_min_samples_leaf(20)
74                .with_n_trees(100)
75                .with_min_samples_split(20),
76        )
77        .with_logistic_settings(LogisticRegressionParameters::default())
78        .with_svc_settings(
79            SVCParameters::default()
80                .with_epoch(10)
81                .with_tol(1e-10)
82                .with_c(1.0)
83                .with_kernel(Kernel::Linear),
84        )
85        .with_decision_tree_classifier_settings(
86            DecisionTreeClassifierParameters::default()
87                .with_min_samples_split(20)
88                .with_max_depth(5)
89                .with_min_samples_leaf(20),
90        )
91        .with_knn_classifier_settings(
92            KNNClassifierParameters::default()
93                .with_algorithm(KNNAlgorithmName::CoverTree)
94                .with_k(3)
95                .with_distance(Distance::Hamming)
96                .with_weight(KNNWeightFunction::Uniform),
97        )
98        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
99        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
100
101    println!("{}", regressor_settings);
102    println!("{}", classifier_settings)
103}
Source

pub fn with_min_samples_leaf( self, min_samples_leaf: usize, ) -> DecisionTreeClassifierParameters

The minimum number of samples required to be at a leaf node.

Examples found in repository?
examples/maximal_classification.rs (line 44)
4fn main() {
5    // Totally customize settings
6    let settings = Settings::default_classification()
7        .with_number_of_folds(3)
8        .shuffle_data(true)
9        .verbose(true)
10        .with_final_model(FinalModel::Blending {
11            algorithm: Algorithm::CategoricalNaiveBayes,
12            meta_training_fraction: 0.15,
13            meta_testing_fraction: 0.15,
14        })
15        .skip(Algorithm::RandomForestClassifier)
16        .sorted_by(Metric::Accuracy)
17        .with_preprocessing(PreProcessing::ReplaceWithPCA {
18            number_of_components: 5,
19        })
20        .with_random_forest_classifier_settings(
21            RandomForestClassifierParameters::default()
22                .with_m(100)
23                .with_max_depth(5)
24                .with_min_samples_leaf(20)
25                .with_n_trees(100)
26                .with_min_samples_split(20),
27        )
28        .with_logistic_settings(
29            LogisticRegressionParameters::default()
30                .with_alpha(1.0)
31                .with_solver(LogisticRegressionSolverName::LBFGS),
32        )
33        .with_svc_settings(
34            SVCParameters::default()
35                .with_epoch(10)
36                .with_tol(1e-10)
37                .with_c(1.0)
38                .with_kernel(Kernel::Linear),
39        )
40        .with_decision_tree_classifier_settings(
41            DecisionTreeClassifierParameters::default()
42                .with_min_samples_split(20)
43                .with_max_depth(5)
44                .with_min_samples_leaf(20),
45        )
46        .with_knn_classifier_settings(
47            KNNClassifierParameters::default()
48                .with_algorithm(KNNAlgorithmName::CoverTree)
49                .with_k(3)
50                .with_distance(Distance::Euclidean)
51                .with_weight(KNNWeightFunction::Uniform),
52        )
53        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
54        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
55
56    // Save the settings for later use
57    settings.save("examples/maximal_classification_settings.yaml");
58
59    // Load a dataset from smartcore and add it to the regressor
60    let mut model =
61        SupervisedModel::new(smartcore::dataset::breast_cancer::load_dataset(), settings);
62
63    // Run a model comparison with all models at default settings
64    model.train();
65
66    // Print the results
67    println!("{}", model);
68
69    // Save teh model for later
70    model.save("examples/maximal_classification_model.aml");
71}
More examples
Hide additional examples
examples/print_settings.rs (line 89)
3fn main() {
4    let regressor_settings = automl::Settings::default_regression()
5        .with_number_of_folds(3)
6        .shuffle_data(true)
7        .verbose(true)
8        .sorted_by(Metric::RSquared)
9        .with_preprocessing(PreProcessing::AddInteractions)
10        .with_linear_settings(
11            LinearRegressionParameters::default().with_solver(LinearRegressionSolverName::QR),
12        )
13        .with_lasso_settings(
14            LassoParameters::default()
15                .with_alpha(10.0)
16                .with_tol(1e-10)
17                .with_normalize(true)
18                .with_max_iter(10_000),
19        )
20        .with_ridge_settings(
21            RidgeRegressionParameters::default()
22                .with_alpha(10.0)
23                .with_normalize(true)
24                .with_solver(RidgeRegressionSolverName::Cholesky),
25        )
26        .with_elastic_net_settings(
27            ElasticNetParameters::default()
28                .with_tol(1e-10)
29                .with_normalize(true)
30                .with_alpha(1.0)
31                .with_max_iter(10_000)
32                .with_l1_ratio(0.5),
33        )
34        .with_knn_regressor_settings(
35            KNNRegressorParameters::default()
36                .with_algorithm(KNNAlgorithmName::CoverTree)
37                .with_k(3)
38                .with_distance(Distance::Euclidean)
39                .with_weight(KNNWeightFunction::Uniform),
40        )
41        .with_svr_settings(
42            SVRParameters::default()
43                .with_eps(1e-10)
44                .with_tol(1e-10)
45                .with_c(1.0)
46                .with_kernel(Kernel::Linear),
47        )
48        .with_random_forest_regressor_settings(
49            RandomForestRegressorParameters::default()
50                .with_m(100)
51                .with_max_depth(5)
52                .with_min_samples_leaf(20)
53                .with_n_trees(100)
54                .with_min_samples_split(20),
55        )
56        .with_decision_tree_regressor_settings(
57            DecisionTreeRegressorParameters::default()
58                .with_min_samples_split(20)
59                .with_max_depth(5)
60                .with_min_samples_leaf(20),
61        );
62
63    let classifier_settings = automl::Settings::default_classification()
64        .with_number_of_folds(3)
65        .shuffle_data(true)
66        .verbose(true)
67        .sorted_by(Metric::Accuracy)
68        .with_preprocessing(PreProcessing::AddInteractions)
69        .with_random_forest_classifier_settings(
70            RandomForestClassifierParameters::default()
71                .with_m(100)
72                .with_max_depth(5)
73                .with_min_samples_leaf(20)
74                .with_n_trees(100)
75                .with_min_samples_split(20),
76        )
77        .with_logistic_settings(LogisticRegressionParameters::default())
78        .with_svc_settings(
79            SVCParameters::default()
80                .with_epoch(10)
81                .with_tol(1e-10)
82                .with_c(1.0)
83                .with_kernel(Kernel::Linear),
84        )
85        .with_decision_tree_classifier_settings(
86            DecisionTreeClassifierParameters::default()
87                .with_min_samples_split(20)
88                .with_max_depth(5)
89                .with_min_samples_leaf(20),
90        )
91        .with_knn_classifier_settings(
92            KNNClassifierParameters::default()
93                .with_algorithm(KNNAlgorithmName::CoverTree)
94                .with_k(3)
95                .with_distance(Distance::Hamming)
96                .with_weight(KNNWeightFunction::Uniform),
97        )
98        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
99        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
100
101    println!("{}", regressor_settings);
102    println!("{}", classifier_settings)
103}
Source

pub fn with_min_samples_split( self, min_samples_split: usize, ) -> DecisionTreeClassifierParameters

The minimum number of samples required to split an internal node.

Examples found in repository?
examples/maximal_classification.rs (line 42)
4fn main() {
5    // Totally customize settings
6    let settings = Settings::default_classification()
7        .with_number_of_folds(3)
8        .shuffle_data(true)
9        .verbose(true)
10        .with_final_model(FinalModel::Blending {
11            algorithm: Algorithm::CategoricalNaiveBayes,
12            meta_training_fraction: 0.15,
13            meta_testing_fraction: 0.15,
14        })
15        .skip(Algorithm::RandomForestClassifier)
16        .sorted_by(Metric::Accuracy)
17        .with_preprocessing(PreProcessing::ReplaceWithPCA {
18            number_of_components: 5,
19        })
20        .with_random_forest_classifier_settings(
21            RandomForestClassifierParameters::default()
22                .with_m(100)
23                .with_max_depth(5)
24                .with_min_samples_leaf(20)
25                .with_n_trees(100)
26                .with_min_samples_split(20),
27        )
28        .with_logistic_settings(
29            LogisticRegressionParameters::default()
30                .with_alpha(1.0)
31                .with_solver(LogisticRegressionSolverName::LBFGS),
32        )
33        .with_svc_settings(
34            SVCParameters::default()
35                .with_epoch(10)
36                .with_tol(1e-10)
37                .with_c(1.0)
38                .with_kernel(Kernel::Linear),
39        )
40        .with_decision_tree_classifier_settings(
41            DecisionTreeClassifierParameters::default()
42                .with_min_samples_split(20)
43                .with_max_depth(5)
44                .with_min_samples_leaf(20),
45        )
46        .with_knn_classifier_settings(
47            KNNClassifierParameters::default()
48                .with_algorithm(KNNAlgorithmName::CoverTree)
49                .with_k(3)
50                .with_distance(Distance::Euclidean)
51                .with_weight(KNNWeightFunction::Uniform),
52        )
53        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
54        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
55
56    // Save the settings for later use
57    settings.save("examples/maximal_classification_settings.yaml");
58
59    // Load a dataset from smartcore and add it to the regressor
60    let mut model =
61        SupervisedModel::new(smartcore::dataset::breast_cancer::load_dataset(), settings);
62
63    // Run a model comparison with all models at default settings
64    model.train();
65
66    // Print the results
67    println!("{}", model);
68
69    // Save teh model for later
70    model.save("examples/maximal_classification_model.aml");
71}
More examples
Hide additional examples
examples/print_settings.rs (line 87)
3fn main() {
4    let regressor_settings = automl::Settings::default_regression()
5        .with_number_of_folds(3)
6        .shuffle_data(true)
7        .verbose(true)
8        .sorted_by(Metric::RSquared)
9        .with_preprocessing(PreProcessing::AddInteractions)
10        .with_linear_settings(
11            LinearRegressionParameters::default().with_solver(LinearRegressionSolverName::QR),
12        )
13        .with_lasso_settings(
14            LassoParameters::default()
15                .with_alpha(10.0)
16                .with_tol(1e-10)
17                .with_normalize(true)
18                .with_max_iter(10_000),
19        )
20        .with_ridge_settings(
21            RidgeRegressionParameters::default()
22                .with_alpha(10.0)
23                .with_normalize(true)
24                .with_solver(RidgeRegressionSolverName::Cholesky),
25        )
26        .with_elastic_net_settings(
27            ElasticNetParameters::default()
28                .with_tol(1e-10)
29                .with_normalize(true)
30                .with_alpha(1.0)
31                .with_max_iter(10_000)
32                .with_l1_ratio(0.5),
33        )
34        .with_knn_regressor_settings(
35            KNNRegressorParameters::default()
36                .with_algorithm(KNNAlgorithmName::CoverTree)
37                .with_k(3)
38                .with_distance(Distance::Euclidean)
39                .with_weight(KNNWeightFunction::Uniform),
40        )
41        .with_svr_settings(
42            SVRParameters::default()
43                .with_eps(1e-10)
44                .with_tol(1e-10)
45                .with_c(1.0)
46                .with_kernel(Kernel::Linear),
47        )
48        .with_random_forest_regressor_settings(
49            RandomForestRegressorParameters::default()
50                .with_m(100)
51                .with_max_depth(5)
52                .with_min_samples_leaf(20)
53                .with_n_trees(100)
54                .with_min_samples_split(20),
55        )
56        .with_decision_tree_regressor_settings(
57            DecisionTreeRegressorParameters::default()
58                .with_min_samples_split(20)
59                .with_max_depth(5)
60                .with_min_samples_leaf(20),
61        );
62
63    let classifier_settings = automl::Settings::default_classification()
64        .with_number_of_folds(3)
65        .shuffle_data(true)
66        .verbose(true)
67        .sorted_by(Metric::Accuracy)
68        .with_preprocessing(PreProcessing::AddInteractions)
69        .with_random_forest_classifier_settings(
70            RandomForestClassifierParameters::default()
71                .with_m(100)
72                .with_max_depth(5)
73                .with_min_samples_leaf(20)
74                .with_n_trees(100)
75                .with_min_samples_split(20),
76        )
77        .with_logistic_settings(LogisticRegressionParameters::default())
78        .with_svc_settings(
79            SVCParameters::default()
80                .with_epoch(10)
81                .with_tol(1e-10)
82                .with_c(1.0)
83                .with_kernel(Kernel::Linear),
84        )
85        .with_decision_tree_classifier_settings(
86            DecisionTreeClassifierParameters::default()
87                .with_min_samples_split(20)
88                .with_max_depth(5)
89                .with_min_samples_leaf(20),
90        )
91        .with_knn_classifier_settings(
92            KNNClassifierParameters::default()
93                .with_algorithm(KNNAlgorithmName::CoverTree)
94                .with_k(3)
95                .with_distance(Distance::Hamming)
96                .with_weight(KNNWeightFunction::Uniform),
97        )
98        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
99        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
100
101    println!("{}", regressor_settings);
102    println!("{}", classifier_settings)
103}

Trait Implementations§

Source§

impl Clone for DecisionTreeClassifierParameters

Source§

fn clone(&self) -> DecisionTreeClassifierParameters

Returns a duplicate of the value. Read more
1.0.0 · Source§

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

Performs copy-assignment from source. Read more
Source§

impl Debug for DecisionTreeClassifierParameters

Source§

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

Formats the value using the given formatter. Read more
Source§

impl Default for DecisionTreeClassifierParameters

Source§

fn default() -> DecisionTreeClassifierParameters

Returns the “default value” for a type. Read more
Source§

impl<'de> Deserialize<'de> for DecisionTreeClassifierParameters

Source§

fn deserialize<__D>( __deserializer: __D, ) -> Result<DecisionTreeClassifierParameters, <__D as Deserializer<'de>>::Error>
where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer. Read more
Source§

impl Serialize for DecisionTreeClassifierParameters

Source§

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. Read more

Auto Trait Implementations§

Blanket Implementations§

Source§

impl<T> Any for T
where T: 'static + ?Sized,

Source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
Source§

impl<T> Borrow<T> for T
where T: ?Sized,

Source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
Source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

Source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
Source§

impl<T> CloneToUninit for T
where T: Clone,

Source§

unsafe fn clone_to_uninit(&self, dest: *mut u8)

🔬This is a nightly-only experimental API. (clone_to_uninit)
Performs copy-assignment from self to dest. Read more
Source§

impl<T> From<T> for T

Source§

fn from(t: T) -> T

Returns the argument unchanged.

Source§

impl<T, U> Into<U> for T
where U: From<T>,

Source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

Source§

impl<T> IntoEither for T

Source§

fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
Source§

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
Source§

impl<T> ToOwned for T
where T: Clone,

Source§

type Owned = T

The resulting type after obtaining ownership.
Source§

fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
Source§

fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
Source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

Source§

type Error = Infallible

The type returned in the event of a conversion error.
Source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
Source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

Source§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
Source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
Source§

impl<V, T> VZip<V> for T
where V: MultiLane<T>,

Source§

fn vzip(self) -> V

Source§

impl<T> DeserializeOwned for T
where T: for<'de> Deserialize<'de>,