kdeexport properties

Kernel Density Estimation (KDE)© uses the Ball Tree or KD Tree algorithms for efficient queries, and combines concepts from unsupervised learning, feature engineering, and data modeling. Neighborbased approaches such as KDE are some of the most popular and useful density estimation techniques. The KDE Modeling and KDE Simulation nodes in SPSS® Modeler expose the core features and commonly used parameters of the KDE library. The nodes are implemented in Python. 
kdeexport properties 
Data type  Property description 

bandwidth 
double  Default is 1 . 
kernel 
string  The kernel to use: gaussian or tophat . Default is
gaussian . 
algorithm 
string  The tree algorithm to use: kd_tree , ball_tree , or
auto . Default is auto . 
metric 
string  The metric to use when calculating distance. For the kd_tree algorithm,
choose from: Euclidean , Chebyshev , Cityblock ,
Minkowski , Manhattan , Infinity ,
P , L2 , or L1 . For the ball_tree
algorithm, choose from: Euclidian , Braycurtis ,
Chebyshev , Canberra , Cityblock ,
Dice , Hamming , Infinity ,
Jaccard , L1 , L2 , Minkowski ,
Matching , Manhattan , P ,
Rogersanimoto , Russellrao , Sokalmichener ,
Sokalsneath , or Kulsinski . Default is
Euclidean . 
atol 
float  The desired absolute tolerance of the result. A larger tolerance will generally lead to
faster execution. Default is 0.0 . 
rtol 
float  The desired relative tolerance of the result. A larger tolerance will generally lead to
faster execution. Default is 1E8 . 
breadthFirst 
boolean  Set to True to use a breadthfirst approach. Set to False
to use a depthfirst approach. Default is True . 
LeafSize 
integer  The leaf size of the underlying tree. Default is 40 . Changing this value may
significantly impact the performance. 
pValue 
double  Specify the P Value to use if you're using Minkowski for the metric. Default
is 1.5 . 