Last updated: Jan 18, 2024
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. 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.
kdemodel 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 following fields as required. |
inputs |
field | List of the field names for input. |
bandwidth |
double | Default is 1. |
kernel |
string | The kernel to use: gaussian, tophat,
epanechnikov, exponential, linear, or
cosine. 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. |
custom_name |
||
default_node_name |
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use_HPO |