LESSBinaryClassifier {less}

LESSBinaryClassifier

Description

Auxiliary binary classifier for Learning with Subset Stacking (LESS)

Value

R6 class of LESSBinaryClassifier

Super classes

less::BaseEstimator -> less::SklearnEstimator -> less::LESSBase -> LESSBinaryClassifier

Methods

Public methods

• LESSBinaryClassifier$new()

• LESSBinaryClassifier$fit()

• LESSBinaryClassifier$predict_proba()

• LESSBinaryClassifier$get_global_estimator()

• LESSBinaryClassifier$set_random_state()

• LESSBinaryClassifier$clone()

Inherited methods

• less::BaseEstimator$get_all_fields()
• less::BaseEstimator$get_attributes()
• less::SklearnEstimator$get_type()
• less::SklearnEstimator$predict()
• less::LESSBase$get_d_normalize()
• less::LESSBase$get_frac()
• less::LESSBase$get_isFitted()
• less::LESSBase$get_n_neighbors()
• less::LESSBase$get_n_replications()
• less::LESSBase$get_n_subsets()
• less::LESSBase$get_random_state()
• less::LESSBase$get_replications()
• less::LESSBase$get_scaling()
• less::LESSBase$get_val_size()

Method new()

Creates a new instance of R6 Class of LESSBinaryClassifier

Usage

LESSBinaryClassifier$new(
  frac = NULL,
  n_neighbors = NULL,
  n_subsets = NULL,
  n_replications = 20,
  d_normalize = TRUE,
  val_size = NULL,
  random_state = NULL,
  tree_method = function(X) KDTree$new(X),
  cluster_method = NULL,
  local_estimator = LinearRegression$new(),
  global_estimator = DecisionTreeClassifier$new(),
  distance_function = NULL,
  scaling = TRUE,
  warnings = TRUE
)

Arguments

frac

fraction of total samples used for the number of neighbors (default is 0.05)

n_neighbors

number of neighbors (default is NULL)

n_subsets

number of subsets (default is NULL)

n_replications

number of replications (default is 20)

d_normalize

distance normalization (default is TRUE)

val_size

percentage of samples used for validation (default is NULL - no validation)

random_state

initialization of the random seed (default is NULL)

tree_method

method used for constructing the nearest neighbor tree, e.g., less::KDTree (default)

cluster_method

method used for clustering the subsets, e.g., less::KMeans (default is NULL)

local_estimator

estimator for the local models (default is less::LinearRegression)

global_estimator

estimator for the global model (default is less::DecisionTreeRegressor)

distance_function

distance function evaluating the distance from a subset to a sample, e.g., df(subset, sample) which returns a vector of distances (default is RBF(subset, sample, 1.0/n_subsets^2))

scaling

flag to normalize the input data (default is TRUE)

warnings

flag to turn on (TRUE) or off (FALSE) the warnings (default is TRUE)

Method fit()

Dummy fit function that calls the proper method according to validation and clustering parameters Options are:

• Default fitting (no validation set, no clustering)

• Fitting with validation set (no clustering)

• Fitting with clustering (no) validation set)

• Fitting with validation set and clustering

Usage

LESSBinaryClassifier$fit(X, y)

Arguments

X

2D matrix or dataframe that includes predictors

y

1D vector or (n,1) dimensional matrix/dataframe that includes response variables

Returns

Fitted R6 Class of LESSBinaryClassifier

Method predict_proba()

Prediction probabilities are evaluated for the test samples in X0

Usage

LESSBinaryClassifier$predict_proba(X0)

Arguments

X0

2D matrix or dataframe that includes predictors

Method get_global_estimator()

Auxiliary function returning the global_estimator

Usage

LESSBinaryClassifier$get_global_estimator()

Method set_random_state()

Auxiliary function that sets random state attribute of the self class

Usage

LESSBinaryClassifier$set_random_state(random_state)

Arguments

random_state

seed number to be set as random state

Returns

self

Method clone()

The objects of this class are cloneable with this method.

Usage

LESSBinaryClassifier$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.