LESSBinaryClassifier: LESSBinaryClassifier

In less: Learning with Subset Stacking

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.

less documentation built on Sept. 27, 2022, 5:05 p.m.