Shulou(Shulou.com)06/02 Report--

This article introduces the "sklearn commonly used API parameter analysis: sklearn.linear_model.LinearRegression" related knowledge, in the actual case of the operation process, many people will encounter such a dilemma, then let the editor lead you to learn how to deal with these situations! I hope you can read it carefully and be able to achieve something!

Sklearn.linear_model.LinearRegression

Call

Sklearn.linear_model.LinearRegression (fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)

Parameters

Fit_intercept

Interpretation: whether to calculate the intercept of the model.

Setting: Bool type, optional, default True, if you use centralized data, you can consider setting it to False, regardless of intercept.

Normalize

Interpretation: whether to standardize the data

Setting: Bool type, optional, default False, it is recommended to put standardization before the training model, by setting sklearn.preprocessing.StandardScaler to achieve, and here set to false when fit_intercept is set to false, this parameter will be automatically ignored. If True, the regression standardizes the input parameters: minus the average and dividing by the corresponding two-norm

Copy_X

Interpretation: whether to copy X

Setting: Bool type, optional, default True. If false, the new data will be overwritten on the original data after centralization and standardization.

N_jobs

Definition: the number of tasks set when calculating. This parameter accelerates the problem that the number of targets > 1 (n_targets > 1) and is large enough.

Setting: int or None, optional, default None. If you choose-1, all CPU will be used.

Attributes

Coef_

Interpretation: the coefficient of feature calculated for linear regression problems

Output: if the input is a multi-objective problem, return a two-dimensional array (n_targets, n_features); if it is a single-objective problem, return an one-dimensional array (n_features,) rank_

Interpretation: the rank of a matrix X is valid only if X is a dense matrix

Output: rank of matrix X

Singular_

Interpretation: the singular value of a matrix X is valid only when X is a dense matrix

Output: array of shape (min (X, y),)

Intercept_

Interpretation: intercept, independent term in linear model

Output: if fit_intercept = False, intercept_ is 0.0

Methods

Fit (self, X, y [, sample_weight])

Training model, sample_weight is the weight value of each sample, default None

Get_params (self [, deep])

Deep defaults to True and returns a dictionary. The key is the parameter name and the value is the estimator parameter value.

Predict (self, X)

Model prediction, return the predicted value

Score (self, X, y [, sample_weight])

Model evaluation, return R ^ 2 coefficient, the optimal value is 1, indicating that all data are predicted correctly

Set_params (self, * * params)

Set the parameters of the estimator, you can modify the parameters and retrain

This is the end of the content of "parsing API parameters commonly used by sklearn: sklearn.linear_model.LinearRegression". Thank you for reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.