Class: TweedieRegressor

Generalized Linear Model with a Tweedie distribution.

This estimator can be used to model different GLMs depending on the power parameter, which determines the underlying distribution.

Constructors

new TweedieRegressor()

new TweedieRegressor(opts?): TweedieRegressor

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.alpha`?	`number`	Constant that multiplies the L2 penalty term and determines the regularization strength. `alpha \= 0` is equivalent to unpenalized GLMs. In this case, the design matrix `X` must have full column rank (no collinearities). Values of `alpha` must be in the range `\[0.0, inf)`.
`opts.fit_intercept`?	`boolean`	Specifies if a constant (a.k.a. bias or intercept) should be added to the linear predictor (`X @ coef + intercept`).
`opts.link`?	`"auto"` \| `"log"` \| `"identity"`	The link function of the GLM, i.e. mapping from linear predictor `X @ coeff + intercept` to prediction `y_pred`. Option ‘auto’ sets the link depending on the chosen `power` parameter as follows:
`opts.max_iter`?	`number`	The maximal number of iterations for the solver. Values must be in the range `\[1, inf)`.
`opts.power`?	`number`	The power determines the underlying target distribution according to the following table:
`opts.solver`?	`"lbfgs"` \| `"newton-cholesky"`	Algorithm to use in the optimization problem:
`opts.tol`?	`number`	Stopping criterion. For the lbfgs solver, the iteration will stop when `max{
`opts.verbose`?	`number`	For the lbfgs solver set verbose to any positive number for verbosity. Values must be in the range `\[0, inf)`.
`opts.warm_start`?	`boolean`	If set to `true`, reuse the solution of the previous call to `fit` as initialization for `coef_` and `intercept_` .

Returns TweedieRegressor

Defined in generated/linear_model/TweedieRegressor.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/linear_model/TweedieRegressor.ts:23
`_isInitialized`	`boolean`	`false`	generated/linear_model/TweedieRegressor.ts:22
`_py`	`PythonBridge`	`undefined`	generated/linear_model/TweedieRegressor.ts:21
`id`	`string`	`undefined`	generated/linear_model/TweedieRegressor.ts:18
`opts`	`any`	`undefined`	generated/linear_model/TweedieRegressor.ts:19

Accessors

coef_

Get Signature

get coef_(): Promise<any[]>

Estimated coefficients for the linear predictor (X @ coef_ + intercept_) in the GLM.

Returns Promise<any[]>

Defined in generated/linear_model/TweedieRegressor.ts:408

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

Names of features seen during fit. Defined only when X has feature names that are all strings.

Returns Promise<ArrayLike>

Defined in generated/linear_model/TweedieRegressor.ts:516

intercept_

Get Signature

get intercept_(): Promise<number>

Intercept (a.k.a. bias) added to linear predictor.

Returns Promise<number>

Defined in generated/linear_model/TweedieRegressor.ts:435

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/linear_model/TweedieRegressor.ts:489

n_iter_

Get Signature

get n_iter_(): Promise<number>

Actual number of iterations used in the solver.

Returns Promise<number>

Defined in generated/linear_model/TweedieRegressor.ts:462

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/linear_model/TweedieRegressor.ts:93

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/linear_model/TweedieRegressor.ts:147

fit()

fit(opts): Promise<any>

Fit a Generalized Linear Model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`	Training data.
`opts.y`?	`ArrayLike`	Target values.

Returns Promise<any>

Defined in generated/linear_model/TweedieRegressor.ts:164

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/linear_model/TweedieRegressor.ts:210

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/linear_model/TweedieRegressor.ts:106

predict()

predict(opts): Promise<any[]>

Predict using GLM with feature matrix X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Samples.

Returns Promise<any[]>

Defined in generated/linear_model/TweedieRegressor.ts:246

score()

score(opts): Promise<number>

Compute D^2, the percentage of deviance explained.

D^2 is a generalization of the coefficient of determination R^2. R^2 uses squared error and D^2 uses the deviance of this GLM, see the User Guide.

D^2 is defined as \(D^2 = 1-\frac{D(y_{true},y_{pred})}{D_{null}}\), \(D_{null}\) is the null deviance, i.e. the deviance of a model with intercept alone, which corresponds to \(y_{pred} = \bar{y}\). The mean \(\bar{y}\) is averaged by sample_weight. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`	Test samples.
`opts.y`?	`ArrayLike`	True values of target.

Returns Promise<number>

Defined in generated/linear_model/TweedieRegressor.ts:284

set_fit_request()

set_fit_request(opts): Promise<any>

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `fit`.

Returns Promise<any>

Defined in generated/linear_model/TweedieRegressor.ts:332

set_score_request()

set_score_request(opts): Promise<any>

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `score`.

Returns Promise<any>

Defined in generated/linear_model/TweedieRegressor.ts:372

TunedThresholdClassifierCV ValidationCurveDisplay