QuantileRegressor

Linear regression model that predicts conditional quantiles.

The linear QuantileRegressor optimizes the pinball loss for a desired quantile and is robust to outliers.

This model uses an L1 regularization like Lasso.

Constructors

constructor()

Signature

new QuantileRegressor(opts?: object): QuantileRegressor;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.alpha?`	`number`	Regularization constant that multiplies the L1 penalty term. `Default Value` `1`
`opts.fit_intercept?`	`boolean`	Whether or not to fit the intercept. `Default Value` `true`
`opts.quantile?`	`number`	The quantile that the model tries to predict. It must be strictly between 0 and 1. If 0.5 (default), the model predicts the 50% quantile, i.e. the median. `Default Value` `0.5`
`opts.solver?`	`"highs-ds"` \| `"highs-ipm"` \| `"highs"` \| `"interior-point"` \| `"revised simplex"`	Method used by `scipy.optimize.linprog` (opens in a new tab) to solve the linear programming formulation. From `scipy>=1.6.0`, it is recommended to use the highs methods because they are the fastest ones. Solvers “highs-ds”, “highs-ipm” and “highs” support sparse input data and, in fact, always convert to sparse csc. From `scipy>=1.11.0`, “interior-point” is not available anymore. `Default Value` `'interior-point'`
`opts.solver_options?`	`any`	Additional parameters passed to `scipy.optimize.linprog` (opens in a new tab) as options. If `undefined` and if `solver='interior-point'`, then `{"lstsq":` true`}` is passed to `scipy.optimize.linprog` (opens in a new tab) for the sake of stability.

Returns

QuantileRegressor

Defined in: generated/linear_model/QuantileRegressor.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/QuantileRegressor.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/QuantileRegressor.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/QuantileRegressor.ts:23 (opens in a new tab)

id

string

Defined in: generated/linear_model/QuantileRegressor.ts:20 (opens in a new tab)

opts

any

Defined in: generated/linear_model/QuantileRegressor.ts:21 (opens in a new tab)

Accessors

coef_

Estimated coefficients for the features.

Signature

coef_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/linear_model/QuantileRegressor.ts:285 (opens in a new tab)

feature_names_in_

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

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/QuantileRegressor.ts:366 (opens in a new tab)

intercept_

The intercept of the model, aka bias term.

Signature

intercept_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/QuantileRegressor.ts:312 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/QuantileRegressor.ts:339 (opens in a new tab)

n_iter_

The actual number of iterations performed by the solver.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/QuantileRegressor.ts:393 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/QuantileRegressor.ts:74 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/linear_model/QuantileRegressor.ts:78 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

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

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/linear_model/QuantileRegressor.ts:133 (opens in a new tab)

fit()

Fit the model according to the given training data.

Signature

fit(opts: object): Promise<any>;

Parameters

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

Returns

Promise<any>

Defined in: generated/linear_model/QuantileRegressor.ts:150 (opens in a new tab)

init()

Initializes the underlying Python resources.

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

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/linear_model/QuantileRegressor.ts:87 (opens in a new tab)

predict()

Predict using the linear model.

Signature

predict(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Samples.

Returns

Promise<any>

Defined in: generated/linear_model/QuantileRegressor.ts:199 (opens in a new tab)

score()

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Signature

score(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n\_samples, n\_samples\_fitted)`, where `n\_samples\_fitted` is the number of samples used in the fitting for the estimator.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True values for `X`.

Returns

Promise<number>

Defined in: generated/linear_model/QuantileRegressor.ts:236 (opens in a new tab)

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