ModelDissector (Mahout Map-Reduce 0.13.0 API)

```
public class ModelDissector
extends Object
```
Uses sample data to reverse engineer a feature-hashed model. The result gives approximate weights for features and interactions in the original space. The idea is that the hashed encoders have the option of having a trace dictionary. This tells us where each feature is hashed to, or each feature/value combination in the case of word-like values. Using this dictionary, we can put values into a synthetic feature vector in just the locations specified by a single feature or interaction. Then we can push this through a linear part of a model to see the contribution of that input. For any generalized linear model like logistic regression, there is a linear part of the model that allows this. What the ModelDissector does is to accept a trace dictionary and a model in an update method. It figures out the weights for the elements in the trace dictionary and stashes them. Then in a summary method, the biggest weights are returned. This update/flush style is used so that the trace dictionary doesn't have to grow to enormous levels, but instead can be cleared between updates.

Nested Classes
Modifier and Type	Class and Description
`static class`	`ModelDissector.Weight`

Constructors
Constructor and Description
`ModelDissector()`

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`List<ModelDissector.Weight>`	`summary(int n)` Returns the n most important features with their weights, most important category and the top few categories that they affect.
`void`	`update(Vector features, Map<String,Set<Integer>> traceDictionary, AbstractVectorClassifier learner)` Probes a model to determine the effect of a particular variable.

Class ModelDissector