{"id":595,"date":"2023-11-09T17:39:49","date_gmt":"2023-11-09T09:39:49","guid":{"rendered":"http:\/\/ai.gitpp.com\/?p=595"},"modified":"2023-11-09T17:39:49","modified_gmt":"2023-11-09T09:39:49","slug":"%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92%e6%98%af%e4%b8%80%e7%a7%8d%e7%94%a8%e4%ba%8e%e8%a7%a3%e5%86%b3%e4%ba%8c%e5%88%86%e7%b1%bb%e9%97%ae%e9%a2%98%e7%9a%84%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e6%96%b9","status":"publish","type":"post","link":"http:\/\/ai.gitpp.com\/index.php\/2023\/11\/09\/%e9%80%bb%e8%be%91%e5%9b%9e%e5%bd%92%e6%98%af%e4%b8%80%e7%a7%8d%e7%94%a8%e4%ba%8e%e8%a7%a3%e5%86%b3%e4%ba%8c%e5%88%86%e7%b1%bb%e9%97%ae%e9%a2%98%e7%9a%84%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e6%96%b9\/","title":{"rendered":"\u903b\u8f91\u56de\u5f52\u662f\u4e00\u79cd\u7528\u4e8e\u89e3\u51b3\u4e8c\u5206\u7c7b\u95ee\u9898\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5"},"content":{"rendered":"\n

\u903b\u8f91\u56de\u5f52\u662f\u4e00\u79cd\u7528\u4e8e\u89e3\u51b3\u4e8c\u5206\u7c7b\u95ee\u9898\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u4e3b\u8981\u7528\u4e8e\u4f30\u8ba1\u67d0\u79cd\u4e8b\u7269\u7684\u53ef\u80fd\u6027\u3002\u5728 Python \u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\u903b\u8f91\u56de\u5f52\uff1a<\/p>\n\n\n\n

    \n
  1. \u5bfc\u5165\u6240\u9700\u5e93\uff1a
    “`python
    import numpy as np
    import pandas as pd<\/li>\n<\/ol>\n\n\n\n
    2. \u51c6\u5907\u6570\u636e\u96c6\uff1a<\/code><\/pre>\n\n\n\n
    python<\/p>\n\n\n\n
    \u8fd9\u91cc\u4ee5\u300a\u673a\u5668\u5b66\u4e60\u5b9e\u6218\u300b\u4e2d\u7684\u903b\u8f91\u56de\u5f52\u5b9e\u4f8b\u4e3a\u4f8b\uff0c\u4f7f\u7528\u759d\u6c14\u75c5\u75c7\u9884\u6d4b\u75c5\u9a6c\u7684\u6b7b\u4ea1\u7387\u6570\u636e\u96c6<\/h1>\n\n\n\n
    data = pd.read_csv(‘horse_cancer.csv’)<\/p>\n\n\n\n
    3. \u6570\u636e\u9884\u5904\u7406\uff1a<\/code><\/pre>\n\n\n\n
    python<\/p>\n\n\n\n
    \u6570\u636e\u9884\u5904\u7406\uff0c\u5982\u5904\u7406\u7f3a\u5931\u503c\u3001\u7f16\u7801\u5206\u7c7b\u53d8\u91cf\u7b49<\/h1>\n\n\n\n
    data = preprocess_data(data)<\/p>\n\n\n\n
    4. \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff1a<\/code><\/pre>\n\n\n\n
    python
    X = data.drop(‘ mortality’, axis=1) # \u63d0\u53d6\u7279\u5f81
    y = data[‘mortality’] # \u63d0\u53d6\u76ee\u6807\u53d8\u91cf
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n\n\n\n
    5. \u5b9e\u73b0\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff1a<\/code><\/pre>\n\n\n\n
    python
    def logistic_regression(X_train, y_train):
    # \u521d\u59cb\u5316\u53c2\u6570
    np.random.seed(42)
    theta = np.zeros(X_train.shape[1] + 1)<\/p>\n\n\n\n
    # \u904d\u5386\u8bad\u7ec3\u6570\u636e\uff0c\u66f4\u65b0\u53c2\u6570  \nfor i in range(1000):  \n    z = np.dot(X_train, theta)  \n    h = 1 \/ (1 + np.exp(-z))  \n    gradient = (1 \/ (X_train.shape[0])) * np.dot(X_train.T, (h - y_train))  \n    theta -= 0.01 * gradient  \n\nreturn theta  <\/code><\/pre>\n\n\n\n
    6. \u6a21\u578b\u8bc4\u4f30\uff1a<\/code><\/pre>\n\n\n\n
    python
    def evaluate_model(theta, X_test, y_test):
    z = np.dot(X_test, theta)
    h = 1 \/ (1 + np.exp(-z))
    accuracy = np.mean(h == y_test)
    return accuracy<\/p>\n\n\n\n
    7. \u8fd0\u884c\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff1a<\/code><\/pre>\n\n\n\n
    python
    theta = logistic_regression(X_train, y_train)
    accuracy = evaluate_model(theta, X_test, y_test)
    print(‘\u903b\u8f91\u56de\u5f52\u6a21\u578b\u51c6\u786e\u7387\uff1a’, accuracy)
    “`
    \u4ee5\u4e0a\u4ee3\u7801\u5b9e\u73b0\u4e86\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u4f7f\u7528\u7ed9\u5b9a\u7684\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u548c\u6d4b\u8bd5\u3002\u8bf7\u6ce8\u610f\uff0c\u8fd9\u4e2a\u4f8b\u5b50\u4ec5\u7528\u4e8e\u6f14\u793a\u903b\u8f91\u56de\u5f52\u7684\u5b9e\u73b0\uff0c\u5b9e\u9645\u5e94\u7528\u4e2d\u53ef\u80fd\u9700\u8981\u6839\u636e\u5177\u4f53\u95ee\u9898\u548c\u6570\u636e\u8fdb\u884c\u76f8\u5e94\u7684\u8c03\u6574\u3002<\/p>\n\n\n\n
    <\/p>\n","protected":false},"excerpt":{"rendered":"
    \u903b\u8f91\u56de\u5f52\u662f\u4e00\u79cd\u7528\u4e8e\u89e3\u51b3\u4e8c\u5206\u7c7b\u95ee\u9898\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\uff0c\u4e3b\u8981\u7528\u4e8e\u4f30\u8ba1\u67d0\u79cd\u4e8b\u7269\u7684\u53ef\u80fd\u6027\u3002\u5728 Python \u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u901a […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-595","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"blocksy_meta":"","_links":{"self":[{"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/posts\/595","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/comments?post=595"}],"version-history":[{"count":1,"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/posts\/595\/revisions"}],"predecessor-version":[{"id":596,"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/posts\/595\/revisions\/596"}],"wp:attachment":[{"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/media?parent=595"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/categories?post=595"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/ai.gitpp.com\/index.php\/wp-json\/wp\/v2\/tags?post=595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}