Class: LinearRegression
Ordinary least squares Linear Regression.
LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.
Constructors
new LinearRegression()
new LinearRegression(
opts?):LinearRegression
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. |
opts.fit_intercept? | boolean | Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered). |
opts.n_jobs? | number | The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly n_targets > 1 and secondly X is sparse or if positive is set to true. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.positive? | boolean | When set to true, forces the coefficients to be positive. This option is only supported for dense arrays. |
Returns LinearRegression
Defined in generated/linear_model/LinearRegression.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/LinearRegression.ts:21 |
_isInitialized | boolean | false | generated/linear_model/LinearRegression.ts:20 |
_py | PythonBridge | undefined | generated/linear_model/LinearRegression.ts:19 |
id | string | undefined | generated/linear_model/LinearRegression.ts:16 |
opts | any | undefined | generated/linear_model/LinearRegression.ts:17 |
Accessors
coef_
Get Signature
get coef_():
Promise<any[]>
Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.
Returns Promise<any[]>
Defined in generated/linear_model/LinearRegression.ts:367
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/LinearRegression.ts:502
intercept_
Get Signature
get intercept_():
Promise<number|any[]>
Independent term in the linear model. Set to 0.0 if fit_intercept \= False.
Returns Promise<number | any[]>
Defined in generated/linear_model/LinearRegression.ts:448
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/LinearRegression.ts:475
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/LinearRegression.ts:54
rank_
Get Signature
get rank_():
Promise<number>
Rank of matrix X. Only available when X is dense.
Returns Promise<number>
Defined in generated/linear_model/LinearRegression.ts:394
singular_
Get Signature
get singular_():
Promise<any[]>
Singular values of X. Only available when X is dense.
Returns Promise<any[]>
Defined in generated/linear_model/LinearRegression.ts:421
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/LinearRegression.ts:108
fit()
fit(
opts):Promise<any>
Fit linear model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Individual weights for each sample. |
opts.X? | ArrayLike | Training data. |
opts.y? | ArrayLike | Target values. Will be cast to X’s dtype if necessary. |
Returns Promise<any>
Defined in generated/linear_model/LinearRegression.ts:125
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/LinearRegression.ts:171
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/LinearRegression.ts:67
predict()
predict(
opts):Promise<any>
Predict using the linear model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Samples. |
Returns Promise<any>
Defined in generated/linear_model/LinearRegression.ts:207
score()
score(
opts):Promise<number>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
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.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/linear_model/LinearRegression.ts:243
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/LinearRegression.ts:291
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>