Snode

Oblique decision tree classifier based on SVM nodes Splitter class

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

Bases: object

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

classmethod copy(node: Splitter.Snode) → Splitter.Snode

get_classifier() → sklearn.svm._classes.SVC

get_down() → Splitter.Snode

get_features() → numpy.array

get_impurity() → float

get_partition_column() → int

get_title() → str

get_up() → Splitter.Snode

is_leaf() → bool

make_predictor()

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)