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 |