{"id":20065,"date":"2026-01-16T15:22:21","date_gmt":"2026-01-16T07:22:21","guid":{"rendered":"https:\/\/92it.top\/?p=20065"},"modified":"2026-01-16T15:22:21","modified_gmt":"2026-01-16T07:22:21","slug":"python-%e5%85%a5%e9%97%a8%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0","status":"publish","type":"post","link":"https:\/\/92it.top\/?p=20065","title":{"rendered":"Python \u5165\u95e8\u673a\u5668\u5b66\u4e60"},"content":{"rendered":"\n<p><strong>\u524d\u8a00\u200b\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>Python \u662f\u673a\u5668\u5b66\u4e60\u4e2d\u6700\u5e38\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u4e4b\u4e00\uff0c\u56e0\u5176\u6613\u4e8e\u5b66\u4e60\u3001\u5f3a\u5927\u7684\u5e93\u652f\u6301\u548c\u793e\u533a\u751f\u6001\u7cfb\u7edf\u3002<\/p>\n\n\n\n<p>\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u9010\u6b65\u8bf4\u660e\u5982\u4f55\u901a\u8fc7 Python \u5165\u95e8\u673a\u5668\u5b66\u4e60\uff0c\u5e76\u4ecb\u7ecd\u9700\u8981\u7684\u4e00\u4e9b\u5e38\u7528\u5e93\u3002<\/p>\n\n\n\n<p><strong>\u5b89\u88c5 Python \u548c\u5fc5\u8981\u7684\u5e93<\/strong><\/p>\n\n\n\n<p><strong>\ud83d\udd39<\/strong>\u65b9\u6cd5\u4e00\uff1a\u5b98\u65b9\u5b89\u88c5\u5668<\/p>\n\n\n\n<p>\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86 Python\uff0c\u4f60\u53ef\u4ee5\u8bbf\u95eePython \u5b98\u65b9\u7f51\u7ad9\u00a0<a href=\"https:\/\/www.python.org\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.python.org\/<\/a>\u00a0\u4e0b\u8f7d\u548c\u5b89\u88c5\u6700\u65b0\u7248\u672c\u3002<\/p>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39<\/strong>\u65b9\u6cd5\u4e8c\uff1aAnaconda \u53d1\u884c\u7248<\/p>\n\n\n\n<p>Anaconda \u662f\u4e13\u4e3a\u6570\u636e\u79d1\u5b66\u8bbe\u8ba1\u7684 Python \u53d1\u884c\u7248\uff0c\u5c31\u50cf\u4e00\u4e2a\u9884\u88c5\u4e86\u6240\u6709\u5de5\u5177\u7684&#8221;\u673a\u5668\u5b66\u4e60\u5de5\u5177\u7bb1&#8221;\u3002<\/p>\n\n\n\n<ul>\n<li>Anaconda \u7684\u4f18\u52bf\uff1a\n<ul>\n<li>\u9884\u88c5\u5e38\u7528\u5e93\uff1aNumPy\u3001Pandas\u3001Scikit-learn \u7b49<\/li>\n\n\n\n<li>\u73af\u5883\u7ba1\u7406\uff1aconda \u547d\u4ee4\u7ba1\u7406\u865a\u62df\u73af\u5883<\/li>\n\n\n\n<li>\u56fe\u5f62\u754c\u9762\uff1aAnaconda Navigator \u63d0\u4f9b\u53ef\u89c6\u5316\u64cd\u4f5c<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul>\n<li>\u8de8\u5e73\u53f0\uff1a\u652f\u6301\u6240\u6709\u4e3b\u6d41\u64cd\u4f5c\u7cfb\u7edf\n<ul>\n<li>\u5b89\u88c5 Anaconda<\/li>\n\n\n\n<li>\u8bbf\u95ee https:\/\/www.anaconda.com\/products\/distribution<\/li>\n\n\n\n<li>\u4e0b\u8f7d\u5bf9\u5e94\u7cfb\u7edf\u7684\u5b89\u88c5\u5305<\/li>\n\n\n\n<li>\u8fd0\u884c\u5b89\u88c5\u7a0b\u5e8f\uff0c\u6309\u63d0\u793a\u5b8c\u6210\u5b89\u88c5<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>\u9a8c\u8bc1\u5b89\u88c5:<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">conda --version\npython --version<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u4e3a\u4ec0\u4e48\u9700\u8981\u865a\u62df\u73af\u5883\uff1f<\/strong><\/p>\n\n\n\n<p>\u865a\u62df\u73af\u5883\u5c31\u50cf\u4e3a\u6bcf\u4e2a\u9879\u76ee\u51c6\u5907\u7684\u72ec\u7acb\u53a8\u623f\uff0c\u907f\u514d\u4e0d\u540c\u9879\u76ee\u7684&#8221;\u8c03\u6599&#8221;\uff08\u5e93\u7248\u672c\uff09\u76f8\u4e92\u5e72\u6270\u3002<\/p>\n\n\n\n<p><a><\/a>\u865a\u62df\u73af\u5883\u7684\u597d\u5904<\/p>\n\n\n\n<ol>\n<li>\u4f9d\u8d56\u9694\u79bb\uff1a\u4e0d\u540c\u9879\u76ee\u4f7f\u7528\u4e0d\u540c\u7248\u672c\u7684\u5e93<\/li>\n\n\n\n<li>\u73af\u5883\u590d\u73b0\uff1a\u65b9\u4fbf\u5728\u5176\u4ed6\u673a\u5668\u4e0a\u91cd\u5efa\u76f8\u540c\u73af\u5883<\/li>\n\n\n\n<li>\u6743\u9650\u7ba1\u7406\uff1a\u907f\u514d\u6c61\u67d3\u7cfb\u7edf Python \u73af\u5883<\/li>\n\n\n\n<li>\u9879\u76ee\u6e05\u7406\uff1a\u5220\u9664\u9879\u76ee\u65f6\u4e00\u5e76\u5220\u9664\u76f8\u5173\u73af\u5883<\/li>\n<\/ol>\n\n\n\n<p>\u4f7f\u7528 conda \u7ba1\u7406\u73af\u5883<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u521b\u5efa\u73af\u5883\nconda create -n ml_env python=3.8\n# \u6fc0\u6d3b\u73af\u5883\nconda activate ml_env\n# \u5b89\u88c5\u5305\nconda install numpy pandas scikit-learn\n# \u5217\u51fa\u73af\u5883\nconda env list\n# \u5220\u9664\u73af\u5883\nconda env remove -n ml_env<\/pre>\n\n\n\n<p>windows python\u539f\u751f\u521b\u5efa\u865a\u62df\u9879\u76ee\u547d\u4ee4<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u521b\u5efa\u73af\u5883\npython -m venv py_env\n\n# \u6fc0\u6d3b\u73af\u5883\npy_env\\Scripts\\activate<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u5f00\u53d1\u5de5\u5177\u914d\u7f6e\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><strong>\ud83d\udd39Jupyter Notebook<\/strong><\/p>\n\n\n\n<p>Jupyter Notebook \u662f\u6570\u636e\u79d1\u5b66\u5bb6\u7684\u6570\u5b57\u5b9e\u9a8c\u5ba4\uff0c\u652f\u6301\u4ea4\u4e92\u5f0f\u7f16\u7a0b\u548c\u53ef\u89c6\u5316\u5c55\u793a\u3002<\/p>\n\n\n\n<p>\u5b89\u88c5\u548c\u542f\u52a8 Jupyter<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u5b89\u88c5 Jupyter\npip install jupyter\n>pip install notebook jupyterlab ipywidgets\n\n# \u542f\u52a8 Jupyter Notebook\njupyter notebook\n\n# \u542f\u52a8 Jupyter Lab\uff08\u66f4\u73b0\u4ee3\u7684\u754c\u9762\uff09\njupyter lab<\/pre>\n\n\n\n<p>Jupyter \u57fa\u672c\u4f7f\u7528<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"1024\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-800x1024.png\" alt=\"\" class=\"wp-image-20067\" style=\"width:396px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-800x1024.png 800w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-234x300.png 234w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-768x983.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-1200x1536.png 1200w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-830x1062.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-230x294.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-350x448.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49-480x614.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-49.png 1216w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/figure><\/div>\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u673a\u5668\u5b66\u4e60\u5e93\u5b89\u88c5\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u5e38\u7528\u673a\u5668\u5b66\u4e60\u5e93\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">pip install numpy pandas matplotlib seaborn scikit-learn<\/pre>\n\n\n\n<p>\u5982\u679c\u4f60\u6253\u7b97\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5b89\u88c5\u5982\u4e0b\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">pip install torch  # \u6216\u8005\npip install tensorflow<\/pre>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"266\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-1024x266.png\" alt=\"\" class=\"wp-image-20068\" style=\"width:630px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-1024x266.png 1024w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-300x78.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-768x199.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-1536x398.png 1536w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-830x215.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-230x60.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-350x91.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50-480x125.png 480w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-50.png 1673w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n<p>\u5728\u4f7f\u7528 Python \u8fdb\u884c\u673a\u5668\u5b66\u4e60\u65f6\uff0c\u6574\u4e2a\u8fc7\u7a0b\u4e00\u822c\u9075\u5faa\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n\n\n\n<ol>\n<li>\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u00a0&#8211; \u4f8b\u5982\uff0cNumPy\u3001Pandas \u548c Scikit-learn\u3002<\/li>\n\n\n\n<li>\u52a0\u8f7d\u548c\u51c6\u5907\u6570\u636e\u00a0&#8211; \u6570\u636e\u662f\u673a\u5668\u5b66\u4e60\u7684\u6838\u5fc3\u3002\u4f60\u9700\u8981\u52a0\u8f7d\u6570\u636e\u5e76\u8fdb\u884c\u5fc5\u8981\u7684\u9884\u5904\u7406\uff08\u4f8b\u5982\u6570\u636e\u6e05\u6d17\u3001\u7f3a\u5931\u503c\u586b\u8865\u7b49\uff09\u3002<\/li>\n\n\n\n<li>\u9009\u62e9\u6a21\u578b\u548c\u7b97\u6cd5\u00a0&#8211; \u6839\u636e\u4efb\u52a1\u9009\u62e9\u9002\u5408\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff08\u5982\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u7b49\uff09\u3002<\/li>\n\n\n\n<li>\u8bad\u7ec3\u6a21\u578b\u00a0&#8211; \u4f7f\u7528\u8bad\u7ec3\u96c6\u6570\u636e\u6765\u8bad\u7ec3\u6a21\u578b\u3002<\/li>\n\n\n\n<li>\u8bc4\u4f30\u6a21\u578b\u00a0&#8211; \u4f7f\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u6a21\u578b\u7684\u51c6\u786e\u6027\uff0c\u5e76\u6839\u636e\u8bc4\u4f30\u7ed3\u679c\u4f18\u5316\u6a21\u578b\u3002<\/li>\n\n\n\n<li>\u8c03\u6574\u6a21\u578b\u548c\u8d85\u53c2\u6570\u00a0&#8211; \u6839\u636e\u8bc4\u4f30\u7ed3\u679c\u8c03\u6574\u6a21\u578b\u7684\u8d85\u53c2\u6570\uff0c\u8fdb\u4e00\u6b65\u4f18\u5316\u6a21\u578b\u6027\u80fd\u3002<\/li>\n<\/ol>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\u4e00\u4e2a\u7b80\u5355\u7684\u673a\u5668\u5b66\u4e60\u4f8b\u5b50\uff1a\u4f7f\u7528 Scikit-learn \u505a\u5206\u7c7b\ud83d\udd16<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>Scikit-learn\uff08\u7b80\u79f0 Sklearn\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5efa\u7acb\u5728 NumPy\u3001SciPy \u548c matplotlib \u8fd9\u4e9b\u79d1\u5b66\u8ba1\u7b97\u5e93\u4e4b\u4e0a\uff0c\u63d0\u4f9b\u4e86\u7b80\u5355\u9ad8\u6548\u7684\u6570\u636e\u6316\u6398\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002<\/p>\n\n\n\n<p>Scikit-learn \u5305\u542b\u4e86\u8bb8\u591a\u5e38\u89c1\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5305\u62ec\uff1a<\/p>\n\n\n\n<ul>\n<li>\u7ebf\u6027\u56de\u5f52\u3001\u5cad\u56de\u5f52\u3001Lasso\u56de\u5f52<\/li>\n\n\n\n<li>\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09<\/li>\n\n\n\n<li>\u51b3\u7b56\u6811\u3001\u968f\u673a\u68ee\u6797\u3001\u68af\u5ea6\u63d0\u5347\u6811<\/li>\n\n\n\n<li>\u805a\u7c7b\u7b97\u6cd5\uff08\u5982K-Means\u3001\u5c42\u6b21\u805a\u7c7b\u3001DBSCAN\uff09<\/li>\n\n\n\n<li>\u964d\u7ef4\u6280\u672f\uff08\u5982PCA\u3001t-SNE\uff09<\/li>\n\n\n\n<li>\u795e\u7ecf\u7f51\u7edc<\/li>\n<\/ul>\n\n\n\n<p>\u63a5\u4e0b\u6765\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u7b80\u5355\u7684\u5206\u7c7b\u4efb\u52a1\u2014\u2014\u4f7f\u7528\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff08Iris Dataset\uff09\u6765\u6f14\u793a\u673a\u5668\u5b66\u4e60\u7684\u6d41\u7a0b\uff0c\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\u662f\u4e00\u4e2a\u7ecf\u5178\u7684\u6570\u636e\u96c6\uff0c\u5305\u542b 150 \u4e2a\u6837\u672c\uff0c\u63cf\u8ff0\u4e86\u4e09\u79cd\u4e0d\u540c\u7c7b\u578b\u7684\u9e22\u5c3e\u82b1\u7684\u82b1\u74e3\u548c\u843c\u7247\u7684\u957f\u5ea6\u548c\u5bbd\u5ea6\u3002<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 1\uff1a\u5bfc\u5165\u5e93<\/strong><\/p>\n\n\n\n<p>\u5bfc\u5165\u9700\u8981\u7684 Python \u5e93\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 2\uff1a\u52a0\u8f7d\u6570\u636e<\/strong><\/p>\n\n\n\n<p>\u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\niris = load_iris()\n\n# \u5c06\u6570\u636e\u8f6c\u5316\u4e3a pandas DataFrame\nX = pd.DataFrame(iris.data, columns=iris.feature_names)  # \u7279\u5f81\u6570\u636e\ny = pd.Series(iris.target)  # \u6807\u7b7e\u6570\u636e\n\n# \u663e\u793a\u524d\u4e94\u884c\u6570\u636e\nprint(X.head())<\/pre>\n\n\n\n<p>\u6253\u5370\u8f93\u51fa\u6570\u636e\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n0                5.1               3.5                1.4               0.2\n1                4.9               3.0                1.4               0.2\n2                4.7               3.2                1.3               0.2\n3                4.6               3.1                1.5               0.2\n4                5.0               3.6                1.4               0.2<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 3\uff1a\u6570\u636e\u96c6\u5212\u5206<\/strong><\/p>\n\n\n\n<p>\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u901a\u5e38\u4f7f\u7528 70% \u8bad\u7ec3\u96c6\u548c 30% \u6d4b\u8bd5\u96c6\u7684\u6bd4\u4f8b\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0880% \u8bad\u7ec3\u96c6\uff0c20% \u6d4b\u8bd5\u96c6\uff09\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 4\uff1a\u7279\u5f81\u7f29\u653e\uff08\u6807\u51c6\u5316\uff09<\/strong><\/p>\n\n\n\n<p>\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u90fd\u4f9d\u8d56\u4e8e\u7279\u5f81\u7684\u5c3a\u5ea6\uff0c\u7279\u522b\u662f\u50cf K \u6700\u8fd1\u90bb\u7b97\u6cd5\u3002\u4e3a\u4e86\u786e\u4fdd\u6bcf\u4e2a\u7279\u5f81\u7684\u5747\u503c\u4e3a 0\uff0c\u6807\u51c6\u5dee\u4e3a 1\uff0c\u6211\u4eec\u4f7f\u7528\u6807\u51c6\u5316\u6765\u5904\u7406\u6570\u636e\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u6807\u51c6\u5316\u7279\u5f81\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 5\uff1a\u9009\u62e9\u6a21\u578b\u5e76\u8bad\u7ec3<\/strong><\/p>\n\n\n\n<p>\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0c\u6211\u4eec\u9009\u62e9 K-Nearest Neighbors\uff08KNN\uff09 \u7b97\u6cd5\u6765\u8fdb\u884c\u5206\u7c7b\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u521b\u5efa KNN \u5206\u7c7b\u5668\nknn = KNeighborsClassifier(n_neighbors=3)\n\n# \u8bad\u7ec3\u6a21\u578b\nknn.fit(X_train, y_train)<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 6\uff1a\u8bc4\u4f30\u6a21\u578b<\/strong><\/p>\n\n\n\n<p>\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u6211\u4eec\u4f7f\u7528\u6d4b\u8bd5\u96c6\u8bc4\u4f30\u6a21\u578b\u7684\u51c6\u786e\u6027\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u9884\u6d4b\u6d4b\u8bd5\u96c6\ny_pred = knn.predict(X_test)\n\n# \u8ba1\u7b97\u51c6\u786e\u7387\naccuracy = accuracy_score(y_test, y_pred)\nprint(f'\u6a21\u578b\u51c6\u786e\u7387: {accuracy:.2f}')<\/pre>\n\n\n\n<p>\u5b8c\u6210\u4ee5\u4e0a\u4ee3\u7801\uff0c\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">\u6a21\u578b\u51c6\u786e\u7387: 1.00<\/pre>\n\n\n\n<p>\u3000\u3000<\/p>\n\n\n\n<p><strong>\ud83d\udd39\u6b65\u9aa4 7\uff1a\u53ef\u89c6\u5316\u7ed3\u679c\uff08\u53ef\u9009\uff09<\/strong><\/p>\n\n\n\n<p>\u4f60\u53ef\u4ee5\u901a\u8fc7\u53ef\u89c6\u5316\u6765\u8fdb\u4e00\u6b65\u4e86\u89e3\u6a21\u578b\u7684\u8868\u73b0\uff0c\u5c24\u5176\u662f\u5728\u591a\u7ef4\u6570\u636e\u96c6\u7684\u60c5\u51b5\u4e0b\u3002\u4f8b\u5982\uff0c\u4f60\u53ef\u4ee5\u7528\u4e8c\u7ef4\u56fe\u6765\u663e\u793a KNN \u5206\u7c7b\u7684\u7ed3\u679c\uff08\u4e0d\u8fc7\u5728\u8fd9\u91cc\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u964d\u7ef4\uff0c\u7b80\u5316\u4e3a\u4e8c\u7ef4\uff09\u3002<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\niris = load_iris()\n\n# \u5c06\u6570\u636e\u8f6c\u5316\u4e3a pandas DataFrame\nX = pd.DataFrame(iris.data, columns=iris.feature_names)  # \u7279\u5f81\u6570\u636e\ny = pd.Series(iris.target)  # \u6807\u7b7e\u6570\u636e\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0880% \u8bad\u7ec3\u96c6\uff0c20% \u6d4b\u8bd5\u96c6\uff09\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \u6807\u51c6\u5316\u7279\u5f81\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n# \u521b\u5efa KNN \u5206\u7c7b\u5668\nknn = KNeighborsClassifier(n_neighbors=3)\n\n# \u8bad\u7ec3\u6a21\u578b\nknn.fit(X_train, y_train)\n\n# \u9884\u6d4b\u6d4b\u8bd5\u96c6\ny_pred = knn.predict(X_test)\n\n# \u8ba1\u7b97\u51c6\u786e\u7387\naccuracy = accuracy_score(y_test, y_pred)\n\n# \u53ef\u89c6\u5316 - \u8fd9\u91cc\u53ea\u662f\u4e00\u4e2a\u7b80\u5355\u793a\u4f8b\uff0c\u5177\u4f53\u53ef\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u9009\u62e9\u7ed8\u56fe\u65b9\u5f0f\nplt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred, cmap='viridis', marker='o')\nplt.title(\"KNN Classification Results\")\nplt.xlabel(\"Feature 1\")\nplt.ylabel(\"Feature 2\")\nplt.show()<\/pre>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"893\" height=\"681\" src=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51.png\" alt=\"\" class=\"wp-image-20069\" style=\"width:476px;height:auto\" srcset=\"https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51.png 893w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51-300x229.png 300w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51-768x586.png 768w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51-830x633.png 830w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51-230x175.png 230w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51-350x267.png 350w, https:\/\/92it.top\/wp-content\/uploads\/2026\/01\/image-51-480x366.png 480w\" sizes=\"(max-width: 893px) 100vw, 893px\" \/><\/figure><\/div>","protected":false},"excerpt":{"rendered":"<p>\u524d\u8a00\u200b\ud83d\udd16 Python \u662f\u673a\u5668\u5b66\u4e60\u4e2d\u6700\u5e38\u7528\u7684\u7f16\u7a0b\u8bed\u8a00\u4e4b\u4e00\uff0c\u56e0\u5176\u6613\u4e8e\u5b66\u4e60\u3001\u5f3a\u5927\u7684\u5e93\u652f\u6301\u548c\u793e\u533a\u751f\u6001\u7cfb\u7edf\u3002 \u63a5\u4e0b\u6765\uff0c [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"_links":{"self":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20065"}],"collection":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20065"}],"version-history":[{"count":2,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20065\/revisions"}],"predecessor-version":[{"id":20070,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20065\/revisions\/20070"}],"wp:attachment":[{"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20065"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20065"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20065"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}