kdeexport properties
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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. |
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 1E-8. |
breadthFirst |
boolean | Set to True to use a breadth-first approach. Set to False
to use a depth-first 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. |