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.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LinearRegression(opts?: object): LinearRegression;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. Default Value true |
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). Default Value true |
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 (opens in a new tab) 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. Default Value false |
Returns
Defined in: generated/linear_model/LinearRegression.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/LinearRegression.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/LinearRegression.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/LinearRegression.ts:19 (opens in a new tab)
id
string
Defined in: generated/linear_model/LinearRegression.ts:16 (opens in a new tab)
opts
any
Defined in: generated/linear_model/LinearRegression.ts:17 (opens in a new tab)
Accessors
coef_
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.
Signature
coef_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/linear_model/LinearRegression.ts:263 (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/LinearRegression.ts:398 (opens in a new tab)
intercept_
Independent term in the linear model. Set to 0.0 if fit\_intercept \= False.
Signature
intercept_(): Promise<number | any[]>;Returns
Promise<number | any[]>
Defined in: generated/linear_model/LinearRegression.ts:344 (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/LinearRegression.ts:371 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/LinearRegression.ts:54 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/LinearRegression.ts:58 (opens in a new tab)
rank_
Rank of matrix X. Only available when X is dense.
Signature
rank_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/LinearRegression.ts:290 (opens in a new tab)
singular_
Singular values of X. Only available when X is dense.
Signature
singular_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/linear_model/LinearRegression.ts:317 (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/LinearRegression.ts:111 (opens in a new tab)
fit()
Fit linear model.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
opts.sample_weight? | ArrayLike | Individual weights for each sample. |
opts.y? | ArrayLike | Target values. Will be cast to X’s dtype if necessary. |
Returns
Promise<any>
Defined in: generated/linear_model/LinearRegression.ts:128 (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/LinearRegression.ts:67 (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/LinearRegression.ts:177 (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/LinearRegression.ts:214 (opens in a new tab)