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kdeexport properties
kdeexport properties
Last updated: Feb 12, 2025
kdeexport properties

KDE Simulation node iconKernel 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. Neighbor-based 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.

Table 1. kdeexport properties
kdeexport properties Data type Property description
custom_fields boolean This option tells the node to use field information specified here instead of that given in any upstream Type node(s). After selecting this option, specify the fields as required.
inputs field List of the field names for input.
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 1E-8.
breadth_first boolean Set to True to use a breadth-first approach. Set to False to use a depth-first approach. Default is True.
leaf_size integer The leaf size of the underlying tree. Default is 40. Changing this value may significantly impact the performance.
p_value double Specify the P Value to use if you're using Minkowski for the metric. Default is 1.5.