{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 安装第三方库\n",
"!pip install scikit-learn pandas matplotlib numpy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import linear_model\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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\n",
"
LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. "
]
},
"metadata": {
"needs_background": "light"
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"output_type": "display_data"
}
],
"source": [
"# 为在Matplotlib中显示中文，设置特殊字体\n",
"plt.rcParams['font.sans-serif'] = ['SimHei']\n",
"plt.rcParams['axes.unicode_minus'] = False\n",
"plt.rcParams.update({'font.size': 13})\n",
"# 创建一个图形框\n",
"fig = plt.figure(figsize=(6, 6), dpi=100)\n",
"# 在图形框里只画一幅图\n",
"ax = fig.add_subplot(111)\n",
"ax.set_title('线性回归示例')\n",
"ax.set_xlabel('$x$')\n",
"ax.set_ylabel('$y$')\n",
"# 画点图，用蓝色圆点表示原始数据\n",
"ax.scatter(data[features], data[labels], color='b', label='真实值: $y = x + \\epsilon$')\n",
"# 根据截距的正负，打印不同的标签\n",
"if model.intercept_ > 0:\n",
" label = f'预测值: $y = {model.coef_.item():.3f}x$ + {model.intercept_.item():.3f}'\n",
"else:\n",
" label = f'预测值: $y = {model.coef_.item():.3f}x$ - {abs(model.intercept_.item()):.3f}'\n",
"# 画线图，用红色线条表示模型结果\n",
"ax.plot(data[features], model.predict(data[features]), color='r', label=label)\n",
"# 设置图例的样式\n",
"legend = plt.legend(shadow=True)\n",
"legend.get_frame().set_facecolor('#6F93AE')\n",
"# 显示均方差和决定系数\n",
"ax.text(0.99, 0.01, f'均方差：{mse:.3f}\\n决定系数：{score:.3f}',\n",
" style='italic', verticalalignment='bottom', horizontalalignment='right',\n",
" transform=ax.transAxes, color='m', fontsize=16)\n",
"plt.savefig('linear_ml.png', dpi=200)\n",
"plt.show()"
]
}
],
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