plot_linear_regression: 绘制线性回归拟合的一种快速方法

一个绘制线性回归拟合的函数。

> 从 mlxtend.plotting 导入 plot_linear_regression

概述

plot_linear_regression 是一个方便的函数，使用 scikit-learn 的 linear_model.LinearRegression 来拟合线性模型，并使用 SciPy 的 stats.pearsonr 来计算相关系数。

参考文献

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示例 1 - 普通最小二乘简单线性回归

import matplotlib.pyplot as plt
from mlxtend.plotting import plot_linear_regression
import numpy as np

X = np.array([4, 8, 13, 26, 31, 10, 8, 30, 18, 12, 20, 5, 28, 18, 6, 31, 12,
   12, 27, 11, 6, 14, 25, 7, 13,4, 15, 21, 15])

y = np.array([14, 24, 22, 59, 66, 25, 18, 60, 39, 32, 53, 18, 55, 41, 28, 61, 35,
   36, 52, 23, 19, 25, 73, 16, 32, 14, 31, 43, 34])

intercept, slope, corr_coeff = plot_linear_regression(X, y)
plt.show()

API

plot_linear_regression(X, y, model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False), corr_func='pearsonr', scattercolor='blue', fit_style='k--', legend=True, xlim='auto')

Plot a linear regression line fit.

Parameters

• X : numpy array, shape = [n_samples,]

  Samples.

• y : numpy array, shape (n_samples,)

  Target values model: object (default: sklearn.linear_model.LinearRegression) Estimator object for regression. Must implement a .fit() and .predict() method. corr_func: str or function (default: 'pearsonr') Uses pearsonr from scipy.stats if corr_func='pearsonr'. to compute the regression slope. If not 'pearsonr', the corr_func, the corr_func parameter expects a function of the form func(, ) as inputs, which is expected to return a tuple (<correlation_coefficient>, <some_unused_value>). scattercolor: string (default: blue) Color of scatter plot points. fit_style: string (default: k--) Style for the line fit. legend: bool (default: True) Plots legend with corr_coeff coef., fit coef., and intercept values. xlim: array-like (x_min, x_max) or 'auto' (default: 'auto') X-axis limits for the linear line fit.

Returns

• regression_fit : tuple

  intercept, slope, corr_coeff (float, float, float)

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/plotting/plot_linear_regression/