一、官方文档
class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)
1.参数：
Ordinary least squares Linear Regression.（普通最小二乘线性回归）
| Parameters: | fit_intercept : boolean, optional, default True。是否计算截距，默认为计算 normalize : boolean, optional, default False。该参数在 copy_X : boolean, optional, default True。If True, X will be copied; else, it may be overwritten. n_jobs : int, optional, default 1 |
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| Attributes: | coef_ : array, shape (n_features, ) or (n_targets, n_features) intercept_ : array |
2、Methods
fit(X, y[, sample_weight])：Fit linear model.get_params([deep])：Get parameters for this estimator.predict(X)：Predict using the linear modelscore(X, y[, sample_weight])：Returns the coefficient of determination R^2 of the prediction.set_params(**params)：Set the parameters of this estimator
（1）Fit linear model.
| Parameters: | X : numpy array or sparse matrix of shape [n_samples,n_features] （Training data） y : numpy array of shape [n_samples, n_targets]。(Target values）. sample_weight : numpy array of shape [n_samples]。每个样本的权重 |
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| Returns: | self : returns an instance of self. |
（2）get_params(deep=True)
| Parameters: | deep : boolean, optional 如果为真将返回估计器的参数 |
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| Returns: | params : mapping of string to any。Parameter names mapped to their values. |
（3）predict（X）
Predict using the linear model
| Parameters: | X : {array-like, sparse matrix}, shape = (n_samples, n_features) |
|---|---|
| Returns: | C : array, shape = (n_samples,).返回预测值 |
（4）score(X, y, sample_weight=None)
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - 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).
| Parameters: | X : array-like, shape = (n_samples, n_features)；Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs)；True values for X. sample_weight : array-like, shape = [n_samples], optional；Sample weights. |
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| Returns: | score : float；R^2 of self.predict(X) wrt. y |
（5）set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
二、代码实现
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20] #去除后面20行
diabetes_X_test = diabetes_X[-20:] #取后面20行
# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
###################################创建线性模型#################################
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error 均方误差
print("Mean squared error: %.2f"
% mean_squared_error(diabetes_y_test, diabetes_y_pred))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()输出结果：
diabetes_y_pred
Out[84]:
array([225.9732401 , 115.74763374, 163.27610621, 114.73638965,
120.80385422, 158.21988574, 236.08568105, 121.81509832,
99.56772822, 123.83758651, 204.73711411, 96.53399594,
154.17490936, 130.91629517, 83.3878227 , 171.36605897,
137.99500384, 137.99500384, 189.56845268, 84.3990668 ])
print('Coefficients: \n', regr.coef_)
Coefficients:
[938.23786125]
print("Mean squared error: %.2f"
% mean_squared_error(diabetes_y_test, diabetes_y_pred))
Mean squared error: 2548.07
print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
Variance score: 0.47
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
Out[88]: <matplotlib.collections.PathCollection at 0x1e435f09550>