Snode

Oblique decision tree classifier based on SVM nodes Splitter class

class Splitter.Snode(clf: SVC, X: ndarray, y: ndarray, features: array, impurity: float, title: str, weight: Optional[ndarray] = None, scaler: Optional[StandardScaler] = None)

Bases: object

Nodes of the tree that keeps the svm classifier and if testing the dataset assigned to it

Parameters

clfSVC

Classifier used

Xnp.ndarray

input dataset in train time (only in testing)

ynp.ndarray

input labes in train time

featuresnp.array

features used to compute hyperplane

impurityfloat

impurity of the node

titlestr

label describing the route to the node

weightnp.ndarray, optional

weights applied to input dataset in train time, by default None

scalerStandardScaler, optional

scaler used if any, by default None

classmethod copy(node: Snode) → Snode

get_classifier() → SVC

get_down() → Snode

get_features() → array

get_impurity() → float

get_partition_column() → int

get_title() → str

get_up() → Snode

graph()

Return a string representing the node in graphviz format

is_leaf() → bool

make_predictor(num_classes: int) → None

Compute the class of the predictor and its belief based on the subdataset of the node only if it is a leaf

set_classifier(clf)

set_down(son)

set_features(features)

set_impurity(impurity)

set_partition_column(col: int)

set_title(title)

set_up(son)