Data Mining Library (23)
Regression (3)¶
Short Description | Function |
---|---|
Apply the result of a linear regression | model.reg.linear |
Propagate uncertainties through a linear regression | model.reg.linearVariance |
Fit and predict a Gaussian Process | model.reg.gaussianProcess |
Decision and Regression Trees (7)¶
Short Description | Function |
---|---|
All-in-one function for simplest case | model.tree.simpleTree |
Simple test function for a tree node | model.tree.simpleTest |
Test function for a tree node with logical operators | model.tree.compoundTest |
Test function with missing value handling | model.tree.missingTest |
Chain of surrogate tests | model.tree.surrogateTest |
Tree walk without explicit missing value handling | model.tree.simpleWalk |
Tree walk with three branches: pass, fail, and missing | model.tree.missingWalk |
Cluster Models (5)¶
Short Description | Function |
---|---|
Closest cluster | model.cluster.closest |
Closest N clusters or N-nearest neighbours | model.cluster.closestN |
Random seeds for online clustering | model.cluster.randomSeeds |
Online clustering with k-means | model.cluster.kmeansIteration |
Update cluster using the mean of data points | model.cluster.updateMean |
Nearest Neighbor Models (3)¶
Short Description | Function |
---|---|
K nearest points | model.neighbor.nearestK |
All points within R | model.neighbor.ballR |
Mean of a sample of points, with weights | model.neighbor.mean |
Naive Bayes (3)¶
Short Description | Function |
---|---|
Bernoulli two-category likelihood | model.naive.bernoulli |
Multinomial multi-category likelihood | model.naive.multinomial |
Gaussian continuous likelihood | model.naive.gaussian |
Neural Networks (1)¶
Short Description | Function |
---|---|
Feedforward neural network organized in layers | model.neural.simpleLayers |
Support Vector Machines (1)¶
Short Description | Function |
---|---|
Basic SVM | model.svm.score |