{"id":927735,"date":"2024-12-26T16:17:54","date_gmt":"2024-12-26T08:17:54","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/927735.html"},"modified":"2024-12-26T16:17:56","modified_gmt":"2024-12-26T08:17:56","slug":"python%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8sklearn","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/927735.html","title":{"rendered":"python\u5982\u4f55\u4f7f\u7528sklearn"},"content":{"rendered":"

\"python\u5982\u4f55\u4f7f\u7528sklearn\"<\/p>\n

\u8981\u5728Python\u4e2d\u4f7f\u7528Scikit-learn\uff08sklearn\uff09\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8be5\u5e93\uff0c\u5bfc\u5165\u6240\u9700\u6a21\u5757\uff0c\u5e76\u6839\u636e\u5177\u4f53\u4efb\u52a1\u9009\u62e9\u9002\u5f53\u7684\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u6216\u5de5\u5177\u3002\u63a5\u7740\uff0c\u51c6\u5907\u548c\u9884\u5904\u7406\u6570\u636e\u3001\u9009\u62e9\u548c\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3001\u5e76\u8fdb\u884c\u9884\u6d4b\u548c\u4f18\u5316\u3002<\/strong> \u5176\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406<\/strong>\u662f\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\uff0c\u786e\u4fdd\u6570\u636e\u8d28\u91cf\u548c\u683c\u5f0f\u7b26\u5408\u6a21\u578b\u8981\u6c42\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002\u4e3a\u6b64\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u7279\u5f81\u9009\u62e9\u548c\u6807\u51c6\u5316\u5904\u7406\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Scikit-learn\u8fdb\u884c\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u4f7f\u7528Scikit-learn\u4e4b\u524d\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5728\u7ec8\u7aef\u6216\u547d\u4ee4\u63d0\u793a\u7b26\u4e2d\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install scikit-learn<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165\u6240\u9700\u6a21\u5757\u3002\u4f8b\u5982\uff0c\u5bfc\u5165\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9664\u4e86\u6a21\u578b\uff0c\u8fd8\u53ef\u4ee5\u5bfc\u5165\u5176\u4ed6\u5de5\u5177\uff0c\u4f8b\u5982\u6570\u636e\u96c6\u3001\u4ea4\u53c9\u9a8c\u8bc1\u548c\u9884\u5904\u7406\u6a21\u5757\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u51c6\u5907\u4e0e\u9884\u5904\u7406<\/h3>\n<\/p>\n
\u6570\u636e\u51c6\u5907\u548c\u9884\u5904\u7406\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u81f3\u5173\u91cd\u8981\u7684\u4e00\u6b65\u3002Scikit-learn\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u5de5\u5177\u6765\u5e2e\u52a9\u5b8c\u6210\u8fd9\u4e00\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n
1. \u6570\u636e\u96c6\u52a0\u8f7d<\/h4>\n<\/p>\n
Scikit-learn\u81ea\u5e26\u4e86\u4e00\u4e9b\u7ecf\u5178\u7684\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u7528\u4e8e\u5b66\u4e60\u548c\u6d4b\u8bd5\u3002\u4f8b\u5982\uff0c\u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6\uff1a<\/p>\n<\/p>\n
from sklearn.datasets import load_iris<\/p>\n
data = load_iris()<\/p>\n
X, y = data.data, data.target<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528\u81ea\u6709\u6570\u636e\u96c6\u65f6\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3002\u6e05\u6d17\u6b65\u9aa4\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u9879\u548c\u7ea0\u6b63\u5f02\u5e38\u503c\u3002Scikit-learn\u7684SimpleImputer<\/code>\u53ef\u4ee5\u7528\u4e8e\u66ff\u6362\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
from sklearn.impute import SimpleImputer<\/p>\n
imputer = SimpleImputer(strategy='mean')<\/p>\n
X = imputer.fit_transform(X)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u7279\u5f81\u9009\u62e9\u4e0e\u6807\u51c6\u5316<\/h4>\n<\/p>\n
\u7279\u5f81\u9009\u62e9\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u6027\u80fd\u5e76\u51cf\u5c11\u8ba1\u7b97\u5f00\u9500\u3002Scikit-learn\u63d0\u4f9b\u4e86SelectKBest<\/code>\u7b49\u5de5\u5177\u6765\u9009\u62e9\u91cd\u8981\u7279\u5f81\uff1a<\/p>\n<\/p>\n
from sklearn.feature_selection import SelectKBest, f_classif<\/p>\n
selector = SelectKBest(score_func=f_classif, k=2)<\/p>\n
X_new = selector.fit_transform(X, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6807\u51c6\u5316\u662f\u53e6\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\uff0c\u53ef\u4ee5\u901a\u8fc7StandardScaler<\/code>\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
scaler = StandardScaler()<\/p>\n
X_scaled = scaler.fit_transform(X_new)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u8bad\u7ec3<\/h3>\n<\/p>\n
\u9009\u62e9\u9002\u5f53\u7684\u6a21\u578b\u662f\u6210\u529f\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u7684\u5173\u952e\u3002Scikit-learn\u63d0\u4f9b\u4e86\u591a\u79cd\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u5982\u7ebf\u6027\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u51b3\u7b56\u6811\u7b49\u3002<\/p>\n<\/p>\n
1. \u7ebf\u6027\u56de\u5f52<\/h4>\n<\/p>\n
\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u4e2a\u7b80\u5355\u800c\u5e38\u7528\u7684\u6a21\u578b\uff0c\u9002\u7528\u4e8e\u9884\u6d4b\u4efb\u52a1\u3002\u4f7f\u7528Scikit-learn\u4e2d\u7684LinearRegression<\/code>\uff1a<\/p>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
model = LinearRegression()<\/p>\n
model.fit(X_scaled, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u652f\u6301\u5411\u91cf\u673a<\/h4>\n<\/p>\n
\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u9002\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u3002\u53ef\u4ee5\u901a\u8fc7SVC<\/code>\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
from sklearn.svm import SVC<\/p>\n
model = SVC(kernel='linear')<\/p>\n
model.fit(X_scaled, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u51b3\u7b56\u6811<\/h4>\n<\/p>\n
\u51b3\u7b56\u6811\u662f\u4e00\u4e2a\u975e\u53c2\u6570\u5316\u6a21\u578b\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\uff1a<\/p>\n<\/p>\n
from sklearn.tree import DecisionTreeClassifier<\/p>\n
model = DecisionTreeClassifier()<\/p>\n
model.fit(X_scaled, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6a21\u578b\u8bc4\u4f30<\/h3>\n<\/p>\n
\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u9700\u8981\u8bc4\u4f30\u5176\u6027\u80fd\u3002Scikit-learn\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u8bc4\u4f30\u5de5\u5177\uff0c\u5982\u51c6\u786e\u7387\u3001\u6df7\u6dc6\u77e9\u9635\u548cROC\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
1. \u51c6\u786e\u7387<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528accuracy_score<\/code>\u6765\u8ba1\u7b97\u6a21\u578b\u7684\u51c6\u786e\u7387\uff1a<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
y_pred = model.predict(X_scaled)<\/p>\n
accuracy = accuracy_score(y, y_pred)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u6df7\u6dc6\u77e9\u9635<\/h4>\n<\/p>\n
\u6df7\u6dc6\u77e9\u9635\u7528\u4e8e\u8bc4\u4f30\u5206\u7c7b\u6a21\u578b\u7684\u6027\u80fd\uff1a<\/p>\n<\/p>\n
from sklearn.metrics import confusion_matrix<\/p>\n
cm = confusion_matrix(y, y_pred)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. ROC\u66f2\u7ebf\u548cAUC<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u4e8c\u5206\u7c7b\u95ee\u9898\uff0cROC\u66f2\u7ebf\u548cAUC\u662f\u91cd\u8981\u7684\u8bc4\u4f30\u6307\u6807\uff1a<\/p>\n<\/p>\n
from sklearn.metrics import roc_curve, auc<\/p>\n
fpr, tpr, _ = roc_curve(y, model.decision_function(X_scaled))<\/p>\n
roc_auc = auc(fpr, tpr)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6a21\u578b\u4f18\u5316<\/h3>\n<\/p>\n
\u5728\u8bc4\u4f30\u9636\u6bb5\uff0c\u5982\u679c\u6a21\u578b\u8868\u73b0\u4e0d\u4f73\uff0c\u53ef\u4ee5\u901a\u8fc7\u8d85\u53c2\u6570\u8c03\u4f18\u548c\u4ea4\u53c9\u9a8c\u8bc1\u8fdb\u884c\u4f18\u5316\u3002<\/p>\n<\/p>\n
1. \u8d85\u53c2\u6570\u8c03\u4f18<\/h4>\n<\/p>\n
Scikit-learn\u63d0\u4f9b\u4e86GridSearchCV<\/code>\u8fdb\u884c\u7f51\u683c\u641c\u7d22\uff0c\u9009\u62e9\u6700\u4f73\u8d85\u53c2\u6570\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import GridSearchCV<\/p>\n
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}<\/p>\n
svc = SVC()<\/p>\n
clf = GridSearchCV(svc, parameters)<\/p>\n
clf.fit(X_scaled, y)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4ea4\u53c9\u9a8c\u8bc1<\/h4>\n<\/p>\n
\u4ea4\u53c9\u9a8c\u8bc1\u662f\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u6807\u51c6\u65b9\u6cd5\uff0ccross_val_score<\/code>\u53ef\u4ee5\u7528\u4e8e\u5b9e\u73b0\uff1a<\/p>\n<\/p>\n
from sklearn.model_selection import cross_val_score<\/p>\n
scores = cross_val_score(model, X_scaled, y, cv=5)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u9884\u6d4b\u4e0e\u7ed3\u679c\u5206\u6790<\/h3>\n<\/p>\n
\u8bad\u7ec3\u548c\u4f18\u5316\u540e\u7684\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u9884\u6d4b\u65b0\u6570\u636e\uff0cScikit-learn\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\uff1a<\/p>\n<\/p>\n
new_data = [[5.1, 3.5, 1.4, 0.2]]<\/p>\n
new_data_scaled = scaler.transform(new_data)<\/p>\n
predictions = model.predict(new_data_scaled)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Scikit-learn\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u4ece\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u9009\u62e9\u3001\u8bad\u7ec3\u3001\u8bc4\u4f30\u5230\u4f18\u5316\u7684\u5168\u5957\u5de5\u5177\u3002\u5728\u4f7f\u7528\u8fc7\u7a0b\u4e2d\uff0c\u6570\u636e\u7684\u8d28\u91cf\u548c\u9884\u5904\u7406\u6b65\u9aa4\u81f3\u5173\u91cd\u8981\uff0c\u76f4\u63a5\u5f71\u54cd\u6a21\u578b\u7684\u6027\u80fd\u548c\u51c6\u786e\u6027\u3002\u540c\u65f6\uff0c\u6a21\u578b\u7684\u9009\u62e9\u548c\u8d85\u53c2\u6570\u8c03\u4f18\u4e5f\u9700\u8981\u6839\u636e\u5177\u4f53\u95ee\u9898\u8fdb\u884c\u8c03\u6574\u3002\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u5e0c\u671b\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5scikit-learn\u5e93\uff1f<\/strong>
\u8981\u5728Python\u4e2d\u4f7f\u7528scikit-learn\uff08\u901a\u5e38\u7b80\u79f0\u4e3asklearn\uff09\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86\u8be5\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528pip\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002\u5728\u547d\u4ee4\u884c\u4e2d\u8f93\u5165pip install scikit-learn<\/code>\uff0c\u5982\u679c\u4f60\u4f7f\u7528\u7684\u662fAnaconda\u73af\u5883\uff0c\u5219\u53ef\u4ee5\u4f7f\u7528conda install scikit-learn<\/code>\u3002\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u6216\u4ea4\u4e92\u5f0f\u73af\u5883\u4e2d\u5bfc\u5165\u8be5\u5e93\uff0c\u4f7f\u7528import sklearn<\/code>\u6765\u5f00\u59cb\u3002<\/p>\n
scikit-learn\u652f\u6301\u54ea\u4e9b\u7c7b\u578b\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff1f<\/strong>
scikit-learn\u63d0\u4f9b\u4e86\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u6db5\u76d6\u4e86\u76d1\u7763\u5b66\u4e60\u548c\u65e0\u76d1\u7763\u5b66\u4e60\u3002\u76d1\u7763\u5b66\u4e60\u5305\u62ec\u5206\u7c7b\uff08\u5982\u903b\u8f91\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u652f\u6301\u5411\u91cf\u673a\u7b49\uff09\u548c\u56de\u5f52\uff08\u5982\u7ebf\u6027\u56de\u5f52\u3001\u968f\u673a\u68ee\u6797\u56de\u5f52\u7b49\uff09\u3002\u65e0\u76d1\u7763\u5b66\u4e60\u5219\u5305\u62ec\u805a\u7c7b\uff08\u5982K\u5747\u503c\u3001\u5c42\u6b21\u805a\u7c7b\u7b49\uff09\u548c\u964d\u7ef4\uff08\u5982\u4e3b\u6210\u5206\u5206\u6790PCA\u7b49\uff09\u3002\u6b64\u5916\uff0cscikit-learn\u8fd8\u652f\u6301\u6a21\u578b\u9009\u62e9\u3001\u6570\u636e\u9884\u5904\u7406\u548c\u8bc4\u4f30\u7b49\u591a\u79cd\u529f\u80fd\u3002<\/p>\n
\u5728\u4f7f\u7528scikit-learn\u8fdb\u884c\u6570\u636e\u5904\u7406\u65f6\uff0c\u6211\u5e94\u8be5\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
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