**ML Models**

The default
ML Model is a nearest centroid classifier. When trained, it calculates and
stores a centroid term vector for each Tag, called the **Trained Term Vector**
(TTV) for the Tag. When used by an ML Task, the Confidence for each Tag is calculated
as a weighted similarity measure between the input term vector and the Tag TTV.
The ML Model also stores other parameters, described below, that are used when
an ML Task executes the Model.

If the ** LSA**
option is selected, then the ML Model partitions the input via Latent Semantic
Analysis (available soon).

Click ** ML
> ML Models **to view, edit, delete and create ML Models.

**Whats Needed**

To create an ML Model, the prerequisite objects are:

· ** Tagset ** contains the possible
ML Model Tag outcomes.

**Creating an ML Model**

Click ** ML
> ML Models **then

· ** Name** for the ML Model and a

· ** LSA? **Select this for a
Latent Semantic Analysis model, which requires no tagset.

· ** Tagset ** Choose an existing
tagset.

· ** Similarity Algorithm **Select
algorithm to calculate similarity & confidence

o **Cosine Similarity**

o **Euclidean Distance**

o **Absolute Error**

· ** Term Count Dampening **Should
term counts be reduced?

o ** Count ** No, use counts
unchanged

o ** Log **Use log2(count)

o ** Binary **count = 0
or 1

· ** IDF Weighting **Are terms
weighted by frequency?

o ** None ** No term weighting

o ** Inverse Document Frequency ** Weight terms higher if they are in a smaller percentage of
examples (higher weight to more unusual terms)

o ** Inverse Tag Frequency **
Weight terms higher if they are in a smaller percentage of Tag TTVs (higher
weight to more unusual terms)

· ** Train Term Count **How to add
to TTV term counts?

o ** Total ** TTV counts are
cumulative over all trainings

o ** Binary **TTV counts
are 0 or 1

· *Use Custom Weights? *

o If checked, then user can upload a spreadsheet of term weights

· *Modify Results Displayed? *

o If checked, then this ML Model stores default values for **Multi-Value**
and **Threshold** see ML Task for more
information.

See ML Task Help for more information on how ML Tasks use ML Models.

See Task Help for other Task parameters.