{"id":3147,"date":"2022-02-27T12:24:47","date_gmt":"2022-02-27T04:24:47","guid":{"rendered":"https:\/\/egonlin.com\/?p=3147"},"modified":"2022-02-27T12:24:47","modified_gmt":"2022-02-27T04:24:47","slug":"%e7%ac%ac%e4%b8%89%e7%af%87%ef%bc%9aadaboost%e7%ae%97%e6%b3%95%e4%bb%a3%e7%a0%81%e9%b8%a2%e5%b0%be%e8%8a%b1%e5%88%86%e7%b1%bb","status":"publish","type":"post","link":"https:\/\/egonlin.com\/?p=3147","title":{"rendered":"\u7b2c\u4e09\u7bc7\uff1aAdaBoost\u7b97\u6cd5\u4ee3\u7801(\u9e22\u5c3e\u82b1\u5206\u7c7b)"},"content":{"rendered":"

AdaBoost\u7b97\u6cd5\u4ee3\u7801(\u9e22\u5c3e\u82b1\u5206\u7c7b)<\/h1>\n

\u5bfc\u5165\u6a21\u5757<\/h1>\n
import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom matplotlib.font_manager import FontProperties\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\n%matplotlib inline\nfont = FontProperties(fname='\/Library\/Fonts\/Heiti.ttc')<\/code><\/pre>\n
\u5bfc\u5165\u6570\u636e<\/h1>\n
X = iris_data.data[:, [2, 3]]\ny = iris_data.target\nlabel_list = ['\u5c71\u9e22\u5c3e', '\u6742\u8272\u9e22\u5c3e', '\u7ef4\u5409\u5c3c\u4e9a\u9e22\u5c3e']<\/code><\/pre>\n
\u6784\u9020\u51b3\u7b56\u8fb9\u754c<\/h1>\n
def plot_decision_regions(X, y, classifier=None):\n    marker_list = ['o', 'x', 's']\n    color_list = ['r', 'b', 'g']\n    cmap = ListedColormap(color_list[:len(np.unique(y))])\n\n    x1_min, x1_max = X[:, 0].min()-1, X[:, 0].max()+1\n    x2_min, x2_max = X[:, 1].min()-1, X[:, 1].max()+1\n    t1 = np.linspace(x1_min, x1_max, 666)\n    t2 = np.linspace(x2_min, x2_max, 666)\n\n    x1, x2 = np.meshgrid(t1, t2)\n    y_hat = classifier.predict(np.array([x1.ravel(), x2.ravel()]).T)\n    y_hat = y_hat.reshape(x1.shape)\n    plt.contourf(x1, x2, y_hat, alpha=0.2, cmap=cmap)\n    plt.xlim(x1_min, x1_max)\n    plt.ylim(x2_min, x2_max)\n\n    for ind, clas in enumerate(np.unique(y)):\n        plt.scatter(X[y == clas, 0], X[y == clas, 1], alpha=0.8, s=50,\n                    c=color_list[ind], marker=marker_list[ind], label=label_list[clas])<\/code><\/pre>\n
\u8bad\u7ec3\u6a21\u578b<\/h1>\n
\u8bad\u7ec3\u6a21\u578b(n_e=10, l_r=0.8)<\/h2>\n
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\u67e5\u770b\u66f4\u591a<\/h2>
\u8054\u7cfb\u7ba1\u7406\u5458\u5fae\u4fe1tutu19192010\uff0c\u6ce8\u518c\u8d26\u53f7<\/p>\n<\/div>
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AdaBoost\u7b97\u6cd5\u4ee3\u7801(\u9e22\u5c3e\u82b1\u5206\u7c7b) \u5bfc\u5165\u6a21\u5757 import numpy as np import matp […]<\/p>\n","protected":false},"author":3,"featured_media":3136,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[276,294,293],"tags":[],"_links":{"self":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3147"}],"collection":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3147"}],"version-history":[{"count":0,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3147\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/media\/3136"}],"wp:attachment":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3147"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3147"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}