\u8bad\u7ec3SVM\u6a21\u578b<\/strong><\/h2>\nmodel = SVC(C=1.0, kernel='rbf', gamma='scale')<\/p>\n
model.fit(X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/p>\n<\/p>\n
\u5728\u8bad\u7ec3\u597d\u6a21\u578b\u540e\uff0c\u9700\u8981\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u53ef\u4ee5\u4f7f\u7528\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u548cF1\u5206\u6570\u7b49\u6307\u6807\u8fdb\u884c\u8bc4\u4f30\u3002<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix<\/p>\n\u9884\u6d4b<\/strong><\/h2>\ny_pred = model.predict(X_test)<\/p>\n
\u8bc4\u4f30<\/strong><\/h2>\naccuracy = accuracy_score(y_test, y_pred)<\/p>\n
print(f"Accuracy: {accuracy:.2f}")<\/p>\n
print("Classification Report:")<\/p>\n
print(classification_report(y_test, y_pred))<\/p>\n
print("Confusion Matrix:")<\/p>\n
print(confusion_matrix(y_test, y_pred))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6a21\u578b\u8c03\u4f18\u4e0e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53c2\u6570\uff08\u4f8b\u5982\u4f7f\u7528\u7f51\u683c\u641c\u7d22\uff09\u6765\u4f18\u5316\u6a21\u578b\u6027\u80fd\uff0c\u5e76\u901a\u8fc7\u53ef\u89c6\u5316\u5de5\u5177\u6765\u5c55\u793a\u7ed3\u679c\u3002<\/p>\n<\/p>\n
\n- \n
\u6a21\u578b\u8c03\u4f18\uff1a<\/strong>\u4f7f\u7528GridSearchCV<\/code>\u6216RandomizedSearchCV<\/code>\u8fdb\u884c\u8d85\u53c2\u6570\u641c\u7d22\u3002<\/p>\n<\/p>\n<\/li>\n- \n
\u53ef\u89c6\u5316\uff1a<\/strong>\u53ef\u4ee5\u901a\u8fc7matplotlib\u7b49\u5e93\u7ed8\u5236\u51b3\u7b56\u8fb9\u754c\u548c\u652f\u6301\u5411\u91cf\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\nfrom sklearn.model_selection import GridSearchCV<\/p>\n\u5b9a\u4e49\u53c2\u6570\u7f51\u683c<\/strong><\/h2>\nparam_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf']}<\/p>\n
\u7f51\u683c\u641c\u7d22<\/strong><\/h2>\ngrid = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)<\/p>\n
grid.fit(X_train, y_train)<\/p>\n
\u8f93\u51fa\u6700\u4f73\u53c2\u6570<\/strong><\/h2>\nprint(f"Best Parameters: {grid.best_params_}")<\/p>\n
\u53ef\u89c6\u5316<\/strong><\/h2>\nimport matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
def plot_decision_boundary(model, X, y):<\/p>\n
h = .02 # step size in the mesh<\/p>\n
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1<\/p>\n
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1<\/p>\n
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),<\/p>\n
np.arange(y_min, y_max, h))<\/p>\n
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])<\/p>\n
Z = Z.reshape(xx.shape)<\/p>\n
plt.contourf(xx, yy, Z, alpha=0.8)<\/p>\n
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')<\/p>\n
plt.xlim(xx.min(), xx.max())<\/p>\n
plt.ylim(yy.min(), yy.max())<\/p>\n
plt.xlabel('Feature 1')<\/p>\n
plt.ylabel('Feature 2')<\/p>\n
plt.title('SVM Decision Boundary')<\/p>\n
plt.show()<\/p>\n
\u4f7f\u7528\u6700\u4f73\u53c2\u6570\u7684\u6a21\u578b\u91cd\u65b0\u8bad\u7ec3<\/strong><\/h2>\nbest_model = SVC(C=grid.best_params_['C'], gamma=grid.best_params_['gamma'], kernel='rbf')<\/p>\n
best_model.fit(X_train, y_train)<\/p>\n
\u53ef\u89c6\u5316\u51b3\u7b56\u8fb9\u754c<\/strong><\/h2>\nplot_decision_boundary(best_model, X_train, y_train)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u603b\u7ed3\uff1a\u5728Python\u4e2d\u4f7f\u7528libsvm\u8fdb\u884cSVM\u6a21\u578b\u7684\u6784\u5efa\u6d89\u53ca\u591a\u4e2a\u6b65\u9aa4\uff0c\u4ece\u5b89\u88c5\u5e93\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u53c2\u6570\u9009\u62e9\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u6027\u80fd\u8bc4\u4f30\u5230\u6a21\u578b\u8c03\u4f18\u4e0e\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u5408\u7406\u7684\u53c2\u6570\u9009\u62e9\u548c\u6570\u636e\u5904\u7406\uff0cSVM\u53ef\u4ee5\u5728\u8bb8\u591a\u5206\u7c7b\u95ee\u9898\u4e2d\u63d0\u4f9b\u4f18\u826f\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n Python\u4e2d\u5982\u4f55\u5b89\u88c5libsvm\u5e93\uff1f<\/strong>
\u8981\u5728Python\u4e2d\u4f7f\u7528libsvm\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip\u547d\u4ee4\u8f7b\u677e\u5b8c\u6210\u5b89\u88c5\u3002\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165pip install libsvm<\/code>\uff0c\u5373\u53ef\u81ea\u52a8\u4e0b\u8f7d\u5e76\u5b89\u88c5\u6700\u65b0\u7248\u672c\u7684libsvm\u3002\u6b64\u5916\uff0c\u786e\u4fdd\u60a8\u7684Python\u73af\u5883\u5df2\u8bbe\u7f6e\u597d\uff0c\u4ee5\u907f\u514d\u5b89\u88c5\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u517c\u5bb9\u6027\u95ee\u9898\u3002<\/p>\nlibsvm\u5728Python\u4e2d\u7684\u57fa\u672c\u7528\u6cd5\u662f\u4ec0\u4e48\uff1f<\/strong>
\u4f7f\u7528libsvm\u8fdb\u884c\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u5efa\u6a21\u65f6\uff0c\u901a\u5e38\u9700\u8981\u51c6\u5907\u8f93\u5165\u6570\u636e\u548c\u6807\u7b7e\u3002\u53ef\u4ee5\u4f7f\u7528svm_train<\/code>\u51fd\u6570\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\uff0csvm_predict<\/code>\u51fd\u6570\u8fdb\u884c\u9884\u6d4b\u3002\u5728\u8bad\u7ec3\u4e4b\u524d\uff0c\u786e\u4fdd\u5c06\u6570\u636e\u8f6c\u6362\u4e3alibsvm\u683c\u5f0f\uff0c\u8fd9\u901a\u5e38\u6d89\u53ca\u5230\u5c06\u7a00\u758f\u7279\u5f81\u8f6c\u6362\u4e3a\u7b26\u5408\u8981\u6c42\u7684\u683c\u5f0f\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\nfrom svmutil import *\ny, x = svm_read_problem('data.txt')\nmodel = svm_train(y, x)\np_label, p_acc, p_val = svm_predict(y, x, model)\n<\/code><\/pre>\nlibsvm\u652f\u6301\u54ea\u4e9b\u7c7b\u578b\u7684\u4efb\u52a1\uff1f<\/strong>
libsvm\u652f\u6301\u591a\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u5305\u62ec\u5206\u7c7b\u3001\u56de\u5f52\u548c\u6982\u7387\u4f30\u8ba1\u3002\u7528\u6237\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u6027\u8d28\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u4e8c\u5206\u7c7b\u6216\u591a\u5206\u7c7b\u95ee\u9898\uff0clibsvm\u63d0\u4f9b\u4e86\u591a\u7c7b\u652f\u6301\uff0c\u800c\u5bf9\u4e8e\u56de\u5f52\u4efb\u52a1\uff0clibsvm\u4e5f\u6709\u76f8\u5e94\u7684\u56de\u5f52\u7b97\u6cd5\u3002\u6b64\u5916\uff0clibsvm\u8fd8\u652f\u6301\u81ea\u5b9a\u4e49\u6838\u51fd\u6570\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u9700\u8981\u521b\u5efa\u7279\u5b9a\u7684\u6838\u51fd\u6570\u4ee5\u63d0\u5347\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u4f7f\u7528libsvm\uff0c\u53ef\u4ee5\u901a\u8fc7\u5b89\u88c5Python\u63a5\u53e3\u5e93\u5982libsvm\u6216scikit-learn\uff0c\u52a0 […]","protected":false},"author":3,"featured_media":937856,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/937855"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=937855"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/937855\/revisions"}],"predecessor-version":[{"id":937857,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/937855\/revisions\/937857"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/937856"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=937855"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=937855"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=937855"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python\u5982\u4f55\u4f7f\u7528libsvm\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/25073518\/a08795dc-d36a-4b91-b9a6-6bc84b2ef3ea.webp\")
<\/p>\n