# RegressionGAM

Generalized additive model (GAM) for regression

## Description

A `RegressionGAM`

object is a
generalized additive model (GAM) object for regression. It is an interpretable model
that explains a response variable using a sum of univariate and bivariate shape
functions.

You can predict responses for new observations by using the `predict`

function,
and plot the effect of each shape function on the prediction (response value) for an
observation by using the `plotLocalEffects`

function. For the full list of object functions for `RegressionGAM`

, see Object Functions.

## Creation

Create a `RegressionGAM`

object by using `fitrgam`

. You can
specify both linear terms and interaction terms for predictors to include univariate shape
functions (predictor trees) and bivariate shape functions (interaction trees) in a trained
model, respectively.

You can update a trained model by using `resume`

or `addInteractions`

.

The

`resume`

function resumes training for the existing terms in a model.The

`addInteractions`

function adds interaction terms to a model that contains only linear terms.

## Properties

### GAM Properties

`BinEdges`

— Bin edges for numeric predictors

cell array of numeric vectors | `[]`

This property is read-only.

Bin edges for numeric predictors, specified as a cell array of *p* numeric vectors, where *p* is the number of predictors. Each vector includes the bin edges for a numeric predictor. The element in the cell array for a categorical predictor is empty because the software does not bin categorical predictors.

The software bins numeric predictors only if you specify the `'NumBins'`

name-value argument as a positive integer scalar when training a model with tree learners.
The `BinEdges`

property is empty if the `'NumBins'`

value is empty (default).

You can reproduce the binned predictor data `Xbinned`

by using the
`BinEdges`

property of the trained model
`mdl`

.

```
X = mdl.X; % Predictor data
Xbinned = zeros(size(X));
edges = mdl.BinEdges;
% Find indices of binned predictors.
idxNumeric = find(~cellfun(@isempty,edges));
if iscolumn(idxNumeric)
idxNumeric = idxNumeric';
end
for j = idxNumeric
x = X(:,j);
% Convert x to array if x is a table.
if istable(x)
x = table2array(x);
end
% Group x into bins by using the
````discretize`

function.
xbinned = discretize(x,[-inf; edges{j}; inf]);
Xbinned(:,j) = xbinned;
end

`Xbinned`

contains the bin indices, ranging from 1 to the number of bins, for numeric predictors.
`Xbinned`

values are 0 for categorical predictors. If
`X`

contains `NaN`

s, then the corresponding
`Xbinned`

values are `NaN`

s.
**Data Types: **`cell`

`Interactions`

— Interaction term indices

two-column matrix of positive integers | `[]`

This property is read-only.

Interaction term indices, specified as a `t`

-by-2 matrix of positive
integers, where `t`

is the number of interaction terms in the model.
Each row of the matrix represents one interaction term and contains the column indexes
of the predictor data `X`

for the interaction term. If the model does
not include an interaction term, then this property is empty
(`[]`

).

The software adds interaction terms to the model in the order of importance based on the
*p*-values. Use this property to check the order of the interaction
terms added to the model.

**Data Types: **`double`

`Intercept`

— Intercept term of model

numeric scalar

This property is read-only.

Intercept (constant) term of the model, which is the sum of the intercept terms in the predictor trees and interaction trees, specified as a numeric scalar.

**Data Types: **`single`

| `double`

`IsStandardDeviationFit`

— Flag indicating whether standard deviation model is fit

`false`

| `true`

Flag indicating whether a model for the standard deviation of the response
variable is fit, specified as `false`

or `true`

.
Specify the `'FitStandardDeviation'`

name-value argument of
`fitrgam`

as `true`

to fit the model for the
standard deviation.

If `IsStandardDeviationFit`

is `true`

, then
you can evaluate the standard deviation at a new observation or at a training
observation of predictor values by using `predict`

or
`resubPredict`

, respectively. These functions also return the prediction
intervals of the response variable, evaluated at given observations.

**Data Types: **`logical`

`ModelParameters`

— Parameters used to train model

model parameter object

This property is read-only.

Parameters used to train the model, specified as a model parameter object.
`ModelParameters`

contains parameter values such as those for the
name-value arguments used to train the model. `ModelParameters`

does
not contain estimated parameters.

Access the fields of `ModelParameters`

by using dot notation. For example,
access the maximum number of decision splits per interaction tree by using
`Mdl.ModelParameters.MaxNumSplitsPerInteraction`

.

`PairDetectionBinEdges`

— Bin edges for interaction term detection

cell array of numeric vectors

This property is read-only.

Bin edges for interaction term detection for numeric predictors, specified as a cell array of *p* numeric vectors, where *p* is the number of predictors. Each vector includes the bin edges for a numeric predictor. The element in the cell array for a categorical predictor is empty because the software does not bin categorical predictors.

To speed up the interaction term detection process, the software bins numeric predictors into at most 8 equiprobable bins. The number of bins can be less than 8 if a predictor has fewer than 8 unique values.

**Data Types: **`cell`

`ReasonForTermination`

— Reason training stops

structure

This property is read-only.

Reason training the model stops, specified as a structure with two fields,
`PredictorTrees`

and `InteractionTrees`

.

Use this property to check if the model contains the specified number of trees for
each linear term (`'NumTreesPerPredictor'`

) and for each interaction term (`'NumTreesPerInteraction'`

). If the `fitrgam`

function terminates training before adding the specified number of trees, this
property contains the reason for the termination.

**Data Types: **`struct`

### Other Regression Properties

`CategoricalPredictors`

— Categorical predictor indices

vector of positive integers | `[]`

This property is read-only.

Categorical predictor
indices, specified as a vector of positive integers. `CategoricalPredictors`

contains index values indicating that the corresponding predictors are categorical. The index
values are between 1 and `p`

, where `p`

is the number of
predictors used to train the model. If none of the predictors are categorical, then this
property is empty (`[]`

).

**Data Types: **`double`

`ExpandedPredictorNames`

— Expanded predictor names

cell array of character vectors

This property is read-only.

Expanded predictor names, specified as a cell array of character vectors.

`ExpandedPredictorNames`

is the same as `PredictorNames`

for a generalized additive model.

**Data Types: **`cell`

`NumObservations`

— Number of observations

numeric scalar

This property is read-only.

Number of observations in the training data stored in `X`

and `Y`

, specified as a numeric scalar.

**Data Types: **`double`

`PredictorNames`

— Predictor variable names

cell array of character vectors

This property is read-only.

Predictor variable names, specified as a cell array of character vectors. The order of the elements of `PredictorNames`

corresponds to the order in which the predictor names appear in the training data.

**Data Types: **`cell`

`ResponseName`

— Response variable name

character vector

This property is read-only.

Response variable name, specified as a character vector.

**Data Types: **`char`

`ResponseTransform`

— Response transformation function

`'none'`

| function handle

Response transformation function, specified as `'none'`

or a function handle.
`ResponseTransform`

describes how the software transforms raw
response values.

For a MATLAB^{®} function or a function that you define, enter its function handle. For
example, you can enter ```
Mdl.ResponseTransform =
@
```

, where
*function*

accepts a numeric vector of the
original responses and returns a numeric vector of the same size containing the
transformed responses.*function*

**Data Types: **`char`

| `function_handle`

`RowsUsed`

— Rows used in fitting

`[]`

| logical vector

This property is read-only.

Rows of the original training data used in fitting the `RegressionGAM`

model,
specified as a logical vector. This property is empty if all rows are used.

**Data Types: **`logical`

`W`

— Observation weights

numeric vector

This property is read-only.

Observation weights used to train the model, specified as an *n*-by-1 numeric
vector. *n* is the number of observations
(`NumObservations`

).

The software normalizes the observation weights specified in the `'Weights'`

name-value argument so that the elements of `W`

sum up to 1.

**Data Types: **`double`

`X`

— Predictors

numeric matrix | table

This property is read-only.

Predictors used to train the model, specified as a numeric matrix or table.

Each row of `X`

corresponds to one observation, and each column corresponds to one variable.

**Data Types: **`single`

| `double`

| `table`

`Y`

— Response

numeric vector

This property is read-only.

Response, specified as a numeric vector.

Each row of `Y`

represents the observed response of the
corresponding row of `X`

.

**Data Types: **`single`

| `double`

### Hyperparameter Optimization Properties

`HyperparameterOptimizationResults`

— Description of cross-validation optimization of hyperparameters

`BayesianOptimization`

object | table

This property is read-only.

Description of the cross-validation optimization of hyperparameters, specified as
a `BayesianOptimization`

object or a table of
hyperparameters and associated values. This property is nonempty when the `'OptimizeHyperparameters'`

name-value argument of
`fitrgam`

is not `'none'`

(default) when the
object is created. The value of `HyperparameterOptimizationResults`

depends on the setting of the `Optimizer`

field in the `HyperparameterOptimizationOptions`

structure of
`fitrgam`

when the object is created.

Value of `Optimizer` Field | Value of `HyperparameterOptimizationResults` |
---|---|

`'bayesopt'` (default) | Object of class `BayesianOptimization` |

`'gridsearch'` or `'randomsearch'` | Table of hyperparameters used, observed objective function values (cross-validation loss), and rank of observations from lowest (best) to highest (worst) |

## Object Functions

### Create `CompactRegressionGAM`

`compact` | Reduce size of machine learning model |

### Create `RegressionPartitionedGAM`

`crossval` | Cross-validate machine learning model |

### Update GAM

`addInteractions` | Add interaction terms to univariate generalized additive model (GAM) |

`resume` | Resume training of generalized additive model (GAM) |

### Interpret Prediction

`lime` | Local interpretable model-agnostic explanations (LIME) |

`partialDependence` | Compute partial dependence |

`plotLocalEffects` | Plot local effects of terms in generalized additive model (GAM) |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

`shapley` | Shapley values |

### Assess Predictive Performance on New Observations

### Assess Predictive Performance on Training Data

`resubPredict` | Predict responses for training data using trained regression model |

`resubLoss` | Resubstitution regression loss |

## Examples

### Train Generalized Additive Model

Train a univariate GAM, which contains linear terms for predictors. Then, interpret the prediction for a specified data instance by using the `plotLocalEffects`

function.

Load the data set `NYCHousing2015`

.

`load NYCHousing2015`

The data set includes 10 variables with information on the sales of properties in New York City in 2015. This example uses these variables to analyze the sale prices (`SALEPRICE`

).

Preprocess the data set. Remove outliers, convert the `datetime`

array (`SALEDATE`

) to the month numbers, and move the response variable (`SALEPRICE`

) to the last column.

idx = isoutlier(NYCHousing2015.SALEPRICE); NYCHousing2015(idx,:) = []; NYCHousing2015.SALEDATE = month(NYCHousing2015.SALEDATE); NYCHousing2015 = movevars(NYCHousing2015,'SALEPRICE','After','SALEDATE');

Display the first three rows of the table.

head(NYCHousing2015,3)

`ans=`*3×10 table*
BOROUGH NEIGHBORHOOD BUILDINGCLASSCATEGORY RESIDENTIALUNITS COMMERCIALUNITS LANDSQUAREFEET GROSSSQUAREFEET YEARBUILT SALEDATE SALEPRICE
_______ ____________ ____________________________ ________________ _______________ ______________ _______________ _________ ________ _________
2 {'BATHGATE'} {'01 ONE FAMILY DWELLINGS'} 1 0 4750 2619 1899 8 0
2 {'BATHGATE'} {'01 ONE FAMILY DWELLINGS'} 1 0 4750 2619 1899 8 0
2 {'BATHGATE'} {'01 ONE FAMILY DWELLINGS'} 1 1 1287 2528 1899 12 0

Train a univariate GAM for the sale prices. Specify the variables for `BOROUGH`

, `NEIGHBORHOOD`

, `BUILDINGCLASSCATEGORY`

, and `SALEDATE`

as categorical predictors.

Mdl = fitrgam(NYCHousing2015,'SALEPRICE','CategoricalPredictors',[1 2 3 9])

Mdl = RegressionGAM PredictorNames: {1x9 cell} ResponseName: 'SALEPRICE' CategoricalPredictors: [1 2 3 9] ResponseTransform: 'none' Intercept: 3.7518e+05 IsStandardDeviationFit: 0 NumObservations: 83517 Properties, Methods

`Mdl`

is a `RegressionGAM`

model object. The model display shows a partial list of the model properties. To view the full list of properties, double-click the variable name `Mdl`

in the Workspace. The Variables editor opens for `Mdl`

. Alternatively, you can display the properties in the Command Window by using dot notation. For example, display the estimated intercept (constant) term of `Mdl`

.

Mdl.Intercept

ans = 3.7518e+05

Predict the sale price for the first observation of the training data, and plot the local effects of the terms in `Mdl`

on the prediction.

yFit = predict(Mdl,NYCHousing2015(1,:))

yFit = 4.4421e+05

plotLocalEffects(Mdl,NYCHousing2015(1,:))

The `predict`

function predicts the sale price for the first observation as `4.4421e5`

. The `plotLocalEffects`

function creates a horizontal bar graph that shows the local effects of the terms in `Mdl`

on the prediction. Each local effect value shows the contribution of each term to the predicted sale price.

### Train GAM with Interaction Terms

Train a generalized additive model that contains linear and interaction terms for predictors in three different ways:

Specify the interaction terms using the

`formula`

input argument.Specify the

`'Interactions'`

name-value argument.Build a model with linear terms first and add interaction terms to the model by using the

`addInteractions`

function.

Load the `carbig`

data set, which contains measurements of cars made in the 1970s and early 1980s.

`load carbig`

Create a table that contains the predictor variables (`Acceleration`

, `Displacement`

, `Horsepower`

, and `Weight`

) and the response variable (`MPG`

).

tbl = table(Acceleration,Displacement,Horsepower,Weight,MPG);

**Specify formula**

Train a GAM that contains the four linear terms (`Acceleration`

, `Displacement`

, `Horsepower`

, and `Weight`

) and two interaction terms (`Acceleration*Displacement`

and `Displacement*Horsepower`

). Specify the terms using a formula in the form `'Y ~ terms'`

.

`Mdl1 = fitrgam(tbl,'MPG ~ Acceleration + Displacement + Horsepower + Weight + Acceleration:Displacement + Displacement:Horsepower');`

The function adds interaction terms to the model in the order of importance. You can use the `Interactions`

property to check the interaction terms in the model and the order in which `fitrgam`

adds them to the model. Display the `Interactions`

property.

Mdl1.Interactions

`ans = `*2×2*
2 3
1 2

Each row of `Interactions`

represents one interaction term and contains the column indexes of the predictor variables for the interaction term.

**Specify 'Interactions'**

Pass the training data (`tbl`

) and the name of the response variable in `tbl`

to `fitrgam`

, so that the function includes the linear terms for all the other variables as predictors. Specify the `'Interactions'`

name-value argument using a logical matrix to include the two interaction terms, `x1*x2`

and `x2*x3`

.

Mdl2 = fitrgam(tbl,'MPG','Interactions',logical([1 1 0 0; 0 1 1 0])); Mdl2.Interactions

`ans = `*2×2*
2 3
1 2

You can also specify `'Interactions'`

as the number of interaction terms or as `'all'`

to include all available interaction terms. Among the specified interaction terms, `fitrgam`

identifies those whose *p*-values are not greater than the `'MaxPValue'`

value and adds them to the model. The default `'MaxPValue'`

is 1 so that the function adds all specified interaction terms to the model.

Specify `'Interactions','all'`

and set the `'MaxPValue'`

name-value argument to 0.05.

Mdl3 = fitrgam(tbl,'MPG','Interactions','all','MaxPValue',0.05);

Warning: Model does not include interaction terms because all interaction terms have p-values greater than the 'MaxPValue' value, or the software was unable to improve the model fit.

Mdl3.Interactions

ans = 0x2 empty double matrix

`Mdl3`

includes no interaction terms, which implies one of the following: all interaction terms have *p*-values greater than 0.05, or adding the interaction terms does not improve the model fit.

**Use addInteractions Function**

Train a univariate GAM that contains linear terms for predictors, and then add interaction terms to the trained model by using the `addInteractions`

function. Specify the second input argument of `addInteractions`

in the same way you specify the `'Interactions'`

name-value argument of `fitrgam`

. You can specify the list of interaction terms using a logical matrix, the number of interaction terms, or `'all'`

.

Specify the number of interaction terms as 3 to add the three most important interaction terms to the trained model.

```
Mdl4 = fitrgam(tbl,'MPG');
UpdatedMdl4 = addInteractions(Mdl4,3);
UpdatedMdl4.Interactions
```

`ans = `*3×2*
2 3
1 2
3 4

`Mdl4`

is a univariate GAM, and `UpdatedMdl4`

is an updated GAM that contains all the terms in `Mdl4`

and three additional interaction terms.

### Resume Training Interaction Trees in GAM

Train a regression GAM that contains both linear and interaction terms. Specify to train the interaction terms for a small number of iterations. After training the interaction terms for more iterations, compare the resubstitution loss.

Load the `carbig`

data set, which contains measurements of cars made in the 1970s and early 1980s.

`load carbig`

Specify `Acceleration`

, `Displacement`

, `Horsepower`

, and `Weight`

as the predictor variables (`X`

) and `MPG`

as the response variable (`Y`

).

X = [Acceleration,Displacement,Horsepower,Weight]; Y = MPG;

Train a GAM that includes all available linear and interaction terms in `X`

. Specify the number of trees per interaction term as 2. `fitrgam`

iterates the boosting algorithm 300 times (default) for linear terms, and iterates the algorithm the specified number of iterations for interaction terms. For each boosting iteration, the function adds one tree per linear term or one tree per interaction term. Specify `'Verbose'`

as 1 to display diagnostic messages at every 10 iterations.

Mdl = fitrgam(X,Y,'Interactions','all','NumTreesPerInteraction',2,'Verbose',1);

|========================================================| | Type | NumTrees | Deviance | RelTol | LearnRate | |========================================================| | 1D| 0| 2.4432e+05| - | - | | 1D| 1| 9507.4| Inf| 1| | 1D| 10| 4470.6| 0.00025206| 1| | 1D| 20| 3895.3| 0.00011448| 1| | 1D| 30| 3617.7| 3.5365e-05| 1| | 1D| 40| 3402.5| 3.7992e-05| 1| | 1D| 50| 3257.1| 2.4983e-05| 1| | 1D| 60| 3131.8| 2.3873e-05| 1| | 1D| 70| 3019.8| 2.2967e-05| 1| | 1D| 80| 2925.9| 2.8071e-05| 1| | 1D| 90| 2845.3| 1.6811e-05| 1| | 1D| 100| 2772.7| 1.852e-05| 1| | 1D| 110| 2707.8| 1.6754e-05| 1| | 1D| 120| 2649.8| 1.651e-05| 1| | 1D| 130| 2596.6| 1.1723e-05| 1| | 1D| 140| 2547.4| 1.813e-05| 1| | 1D| 150| 2501.1| 1.8659e-05| 1| | 1D| 160| 2455.7| 1.386e-05| 1| | 1D| 170| 2416.9| 1.0615e-05| 1| | 1D| 180| 2377.2| 8.534e-06| 1| | 1D| 190| 2339| 7.6771e-06| 1| | 1D| 200| 2303.3| 9.5866e-06| 1| | 1D| 210| 2270.7| 8.4276e-06| 1| | 1D| 220| 2240.1| 8.5778e-06| 1| | 1D| 230| 2209.2| 9.6761e-06| 1| | 1D| 240| 2178.7| 7.0622e-06| 1| | 1D| 250| 2150.3| 8.3082e-06| 1| | 1D| 260| 2122.3| 7.9542e-06| 1| | 1D| 270| 2097.7| 7.6328e-06| 1| | 1D| 280| 2070.4| 9.4322e-06| 1| | 1D| 290| 2044.3| 7.5722e-06| 1| | 1D| 300| 2019.7| 6.6719e-06| 1| |========================================================| | Type | NumTrees | Deviance | RelTol | LearnRate | |========================================================| | 2D| 0| 2019.7| - | - | | 2D| 1| 1795.5| 0.0005975| 1| | 2D| 2| 1523.4| 0.0010079| 1|

To check whether `fitrgam`

trains the specified number of trees, display the `ReasonForTermination`

property of the trained model and view the displayed messages.

Mdl.ReasonForTermination

`ans = `*struct with fields:*
PredictorTrees: 'Terminated after training the requested number of trees.'
InteractionTrees: 'Terminated after training the requested number of trees.'

Compute the regression loss for the training data.

resubLoss(Mdl)

ans = 3.8277

Resume training the model for another 100 iterations. Because `Mdl`

contains both linear and interaction terms, the `resume`

function resumes training for the interaction terms and adds more trees for them (interaction trees).

UpdatedMdl = resume(Mdl,100);

|========================================================| | Type | NumTrees | Deviance | RelTol | LearnRate | |========================================================| | 2D| 0| 1523.4| - | - | | 2D| 1| 1363.9| 0.00039695| 1| | 2D| 10| 594.04| 8.0295e-05| 1| | 2D| 20| 359.44| 4.3201e-05| 1| | 2D| 30| 238.51| 2.6869e-05| 1| | 2D| 40| 153.98| 2.6271e-05| 1| | 2D| 50| 91.464| 8.0936e-06| 1| | 2D| 60| 61.882| 3.8528e-06| 1| | 2D| 70| 43.206| 5.9888e-06| 1|

UpdatedMdl.ReasonForTermination

`ans = `*struct with fields:*
PredictorTrees: 'Terminated after training the requested number of trees.'
InteractionTrees: 'Unable to improve the model fit.'

`resume`

terminates training when adding more trees does not improve the deviance of the model fit.

Compute the regression loss using the updated model.

resubLoss(UpdatedMdl)

ans = 0.0944

The regression loss decreases after `resume`

updates the model with more iterations.

## More About

### Generalized Additive Model (GAM) for Regression

A generalized additive model (GAM) is an interpretable model that explains a response variable using a sum of univariate and bivariate shape functions of predictors.

`fitrgam`

uses a boosted tree as a shape function for each predictor and, optionally, each pair of predictors; therefore, the function can capture a nonlinear relation between a predictor and the response variable. Because contributions of individual shape functions to the prediction (response value) are well separated, the model is easy to interpret.

The standard GAM uses a univariate shape function for each predictor.

$$\begin{array}{l}y~N\left(\mu ,{\sigma}^{2}\right)\\ g(\mu )=\mu =c+\text{}{f}_{1}({x}_{1})+\text{}{f}_{2}({x}_{2})+\cdots +{f}_{p}({x}_{p}),\end{array}$$

where *y* is a response variable that follows the normal distribution with mean *μ* and standard deviation *σ*. *g*(*μ*) is an identity link function, and *c* is an intercept (constant) term. *f _{i}*(

*x*) is a univariate shape function for the

_{i}*i*th predictor, which is a boosted tree for a linear term for the predictor (predictor tree).

You can include interactions between predictors in a model by adding bivariate shape functions of important interaction terms to the model.

$$\mu =c+\text{}{f}_{1}({x}_{1})+\text{}{f}_{2}({x}_{2})+\cdots +{f}_{p}({x}_{p})+{\displaystyle \sum _{i,j\in \{1,2,\cdots ,p\}}{f}_{ij}({x}_{i}{x}_{j})},$$

where *f _{ij}*(

*x*

_{i}*x*) is a bivariate shape function for the

_{j}*i*th and

*j*th predictors, which is a boosted tree for an interaction term for the predictors (interaction tree).

`fitrgam`

finds important interaction terms based on the *p*-values of *F*-tests. For details, see Interaction Term Detection.

If you specify `'FitStandardDeviation'`

of `fitrgam`

as
`false`

(default), then `fitrgam`

trains a model for
the mean *μ*. If you specify `'FitStandardDeviation'`

as
`true`

, then `fitrgam`

trains an additional model
for the standard deviation *σ* and sets the
`IsStandardDeviationFit`

property of the GAM object to
`true`

.

## References

[1] Lou, Yin, Rich Caruana, and Johannes Gehrke. "Intelligible Models for Classification and Regression." *Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’12).* Beijing, China: ACM Press, 2012, pp. 150–158.

[2] Lou, Yin, Rich Caruana, Johannes Gehrke, and Giles Hooker. "Accurate Intelligible Models with Pairwise Interactions." *Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’13)* Chicago, Illinois, USA: ACM Press, 2013, pp. 623–631.

## See Also

`CompactRegressionGAM`

| `RegressionPartitionedGAM`

| `fitrgam`

| `resume`

| `addInteractions`

**Introduced in R2021a**

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