HuberRegressor
L2-regularized linear regression model that is robust to outliers.
The Huber Regressor optimizes the squared loss for the samples where |(y \- Xw \- c) / sigma| < epsilon and the absolute loss for the samples where |(y \- Xw \- c) / sigma| > epsilon, where the model coefficients w, the intercept c and the scale sigma are parameters to be optimized. The parameter sigma makes sure that if y is scaled up or down by a certain factor, one does not need to rescale epsilon to achieve the same robustness. Note that this does not take into account the fact that the different features of X may be of different scales.
The Huber loss function has the advantage of not being heavily influenced by the outliers while not completely ignoring their effect.
Read more in the User Guide
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new HuberRegressor(opts?: object): HuberRegressor;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Strength of the squared L2 regularization. Note that the penalty is equal to alpha \* ||w||^2. Must be in the range \0, inf). Default Value 0.0001 |
opts.epsilon? | number | The parameter epsilon controls the number of samples that should be classified as outliers. The smaller the epsilon, the more robust it is to outliers. Epsilon must be in the range \[1, inf). Default Value 1.35 |
opts.fit_intercept? | boolean | Whether or not to fit the intercept. This can be set to false if the data is already centered around the origin. Default Value true |
opts.max_iter? | number | Maximum number of iterations that scipy.optimize.minimize(method="L-BFGS-B") should run for. Default Value 100 |
opts.tol? | number | The iteration will stop when max{|proj g\_i | i \= 1, ..., n} <= tol where pg_i is the i-th component of the projected gradient. Default Value 0.00001 |
opts.warm_start? | boolean | This is useful if the stored attributes of a previously used model has to be reused. If set to false, then the coefficients will be rewritten for every call to fit. See [the Glossary. Default Value false |
Returns
Defined in: generated/linear_model/HuberRegressor.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/HuberRegressor.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/HuberRegressor.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/HuberRegressor.ts:23 (opens in a new tab)
id
string
Defined in: generated/linear_model/HuberRegressor.ts:20 (opens in a new tab)
opts
any
Defined in: generated/linear_model/HuberRegressor.ts:21 (opens in a new tab)
Accessors
coef_
Features got by optimizing the L2-regularized Huber loss.
Signature
coef_(): Promise<any>;Returns
Promise<any>
Defined in: generated/linear_model/HuberRegressor.ts:277 (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/HuberRegressor.ts:373 (opens in a new tab)
intercept_
Bias.
Signature
intercept_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/HuberRegressor.ts:300 (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/HuberRegressor.ts:348 (opens in a new tab)
n_iter_
Number of iterations that scipy.optimize.minimize(method="L-BFGS-B") has run for.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/HuberRegressor.ts:398 (opens in a new tab)
outliers_
A boolean mask which is set to true where the samples are identified as outliers.
Signature
outliers_(): Promise<any>;Returns
Promise<any>
Defined in: generated/linear_model/HuberRegressor.ts:423 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/HuberRegressor.ts:74 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/HuberRegressor.ts:78 (opens in a new tab)
scale_
The value by which |y \- Xw \- c| is scaled down.
Signature
scale_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/HuberRegressor.ts:325 (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/HuberRegressor.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 vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.sample_weight? | ArrayLike | Weight given to each sample. |
opts.y? | ArrayLike | Target vector relative to X. |
Returns
Promise<any>
Defined in: generated/linear_model/HuberRegressor.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/HuberRegressor.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/HuberRegressor.ts:195 (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/HuberRegressor.ts:230 (opens in a new tab)