# removeLearners

Remove members of compact regression ensemble

## Syntax

``cens1 = removeLearners(cens,idx)``

## Description

example

````cens1 = removeLearners(cens,idx)` creates a compact regression ensemble identical to `cens` excluding the ensemble members in the `idx` vector.```

## Examples

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Create a compact regression ensemble. Compact it further by removing members of the ensemble.

Load the `carsmall` data set and select `Weight` and `Cylinders` as predictors.

```load carsmall X = [Weight Cylinders];```

Train a regression ensemble using LSBoost. Specify tree stumps as the weak learners.

```t = templateTree(MaxNumSplits=1); ens = fitrensemble(X,MPG,Method="LSBoost",Learners=t,... CategoricalPredictors=2);```

Create a compact classification ensemble `cens` from `ens`.

`cens = compact(ens);`

Remove the last 50 members of the ensemble.

```idx = cens.NumTrained-49:cens.NumTrained; cens1 = removeLearners(cens,idx);```

## Input Arguments

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Compact regression ensemble model, specified as a `CompactRegressionEnsemble` model object created with `compact`.

Indices of learners to remove, specified as a vector of positive integers with entries in the range `1` to `cens.NumTrained`, where `cens.NumTrained` is the number of members in `cens`. `cens1` contains all members of `cens` except those with indices in `idx`.

Typically, you set `idx = j:cens.NumTrained` for some positive integer `j`.

Example: `idx=[3:5]`

Data Types: `single` | `double`

## Tips

• Removing learners reduces the memory used by the ensemble and speeds up its predictions.

• To retain just one ensemble, set `cens1` equal to `cens`.

## Version History

Introduced in R2011a