Description Usage Arguments Details Value

Deploy a model to predict outcomes from the data.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## S4 method for signature 'ExprsMachine'
predict(object, array, verbose = TRUE)
## S4 method for signature 'ExprsModule'
predict(object, array, verbose = TRUE)
## S4 method for signature 'RegrsModel'
predict(object, array, verbose = TRUE)
## S4 method for signature 'ExprsEnsemble'
predict(object, array, how = "probability",
verbose = TRUE)
``` |

`object` |
An |

`array` |
An |

`verbose` |
A logical scalar. Argument passed to |

`how` |
A character string. Select from "probability" or "majority". See Details. Argument applies to binary classifier ensembles only. |

Models can only get deployed on an object of the type used to build
the model. Binary classification and regression are handled natively
by the machine learning algorithm chosen. Multi-class classification
is handled by `doMulti`

. Note that a validation set
should never get modified once separated from the training set.
See `buildEnsemble`

to learn about ensembles.

For binary classifier ensembles, when `how = "probability"`

, outcomes
are based on the average class probability (via `@probability`

)
estimated by each deployed model. When `how = "majority"`

, outcomes
are based on consensus voting whereby each deployed model casts a single
(all-or-nothing) vote (via `@pred`

) in a winner takes all approach.
In both scenarios, ties get broken randomly (as weighted by class).

For multi-class classifier ensembles, outcomes are based on the
`how = "majority"`

method from above. For regression ensembles,
outcomes are based on the average predicted value.

Returns an `ExprsPredict`

or `RegrsPredict`

object.

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