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Compute partial dependence

computes the partial dependence `pd`

= partialDependence(`RegressionMdl`

,`Vars`

)`pd`

between the predictor variables
listed in `Vars`

and the responses predicted by using the regression
model `RegressionMdl`

, which contains predictor data.

computes the partial dependence `pd`

= partialDependence(`ClassificationMdl`

,`Vars`

,`Labels`

)`pd`

between the predictor variables
listed in `Vars`

and the scores for the classes specified in
`Labels`

by using the classification model
`ClassificationMdl`

, which contains predictor data.

uses additional options specified by one or more name-value pair arguments. For example,
if you specify `pd`

= partialDependence(___,`Name,Value`

)`'UseParallel','true'`

, the
`partialDependence`

function uses parallel computing to perform the
partial dependence calculations.

`partialDependence`

uses a `predict`

function to
predict responses or scores. `partialDependence`

chooses the proper
`predict`

function according to the model
(`RegressionMdl`

or `ClassificationMdl`

) and runs
`predict`

with its default settings. For details about each
`predict`

function, see the `predict`

functions in the
following two tables. If the specified model is a tree-based model (not including a boosted
ensemble of trees), then `partialDependence`

uses the weighted traversal
algorithm instead of the `predict`

function. For details, see Weighted Traversal Algorithm.

**Regression Model Object**

Model Type | Full or Compact Regression Model Object | Function to Predict Responses |
---|---|---|

Bootstrap aggregation for ensemble of decision trees | `CompactTreeBagger` | `predict` |

Bootstrap aggregation for ensemble of decision trees | `TreeBagger` | `predict` |

Ensemble of regression models | `RegressionEnsemble` , `RegressionBaggedEnsemble` , `CompactRegressionEnsemble` | `predict` |

Gaussian kernel regression model using random feature expansion | `RegressionKernel` | `predict` |

Gaussian process regression | `RegressionGP` , `CompactRegressionGP` | `predict` |

Generalized additive model | `RegressionGAM` , `CompactRegressionGAM` | `predict` |

Generalized linear mixed-effect model | `GeneralizedLinearMixedModel` | `predict` |

Generalized linear model | `GeneralizedLinearModel` , `CompactGeneralizedLinearModel` | `predict` |

Linear mixed-effect model | `LinearMixedModel` | `predict` |

Linear regression | `LinearModel` , `CompactLinearModel` | `predict` |

Linear regression for high-dimensional data | `RegressionLinear` | `predict` |

Neural network regression model | `RegressionNeuralNetwork` , `CompactRegressionNeuralNetwork` | `predict` |

Nonlinear regression | `NonLinearModel` | `predict` |

Regression tree | `RegressionTree` , `CompactRegressionTree` | `predict` |

Support vector machine | `RegressionSVM` , `CompactRegressionSVM` | `predict` |

**Classification Model Object**

`plotPartialDependence`

computes and plots partial dependence values. The function can also create individual conditional expectation (ICE) plots.

[2] Hastie, Trevor, Robert Tibshirani,
and Jerome Friedman. *The Elements of Statistical Learning. New York*,
NY: Springer New York, 2009.

`lime`

| `oobPermutedPredictorImportance`

| `plotPartialDependence`

| `predictorImportance (RegressionEnsemble)`

| `predictorImportance (RegressionTree)`

| `relieff`

| `sequentialfs`

| `shapley`