{"id":465,"date":"2019-09-17T20:09:00","date_gmt":"2019-09-17T12:09:00","guid":{"rendered":"http:\/\/note.systw.net\/note\/?p=465"},"modified":"2023-11-02T20:12:13","modified_gmt":"2023-11-02T12:12:13","slug":"sklearn-logistic-regression","status":"publish","type":"post","link":"https:\/\/systw.net\/note\/archives\/465","title":{"rendered":"SKLearn Logistic Regression"},"content":{"rendered":"\n

Logistic Regression
\u5c6c\u65bc\u5206\u985e\u6f14\u7b97\u6cd5\uff0c\u900f\u904eregression\u65b9\u5f0f\u9054\u5230\u5224\u65b7\u985e\u5225\u7684\u6548\u679c
http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression<\/p>\n\n\n\n


\u8f09\u5165\u6a21\u7d44<\/strong>
from sklearn import linear_model<\/p>\n\n\n\n

\u5efa\u7acb\u521d\u59cbmodel<\/strong>
< model>=linear_model.LogisticRegression(penalty=’l2′, dual=False, tol=0.0001, C=1.0, fit_intercept=True,intercept_scaling=1, class_weight=None, random_state=None, solver=’liblinear’, max_iter=100, multi_class=’ovr’,verbose=0, warm_start=False, n_jobs=1)
\u5e38\u7528\u53c3\u6578\u5982\u4e0b
\u3000C : float, optional (default=1.0)
\u3000\u3000\u8a72\u53c3\u6578\u7528\u65bc\u63a7\u5236overfitting\u7a0b\u5ea6,\u6578\u5b57\u8d8a\u5927,Regularization\u5f37\u5ea6\u8d8a\u4f4e<\/p>\n\n\n\n


\u8b93model\u5b78\u7fd2<\/strong>
< model>.fit(input, output)<\/p>\n\n\n\n


\u6839\u64dainput\u9810\u6e2coutput class<\/strong>
< model>.predict(input)
ps:
predict_log_proba(X) # Log of probability estimates.
predict_proba(X) # Probability estimates.<\/p>\n\n\n\n

\u3000<\/p>\n\n\n\n

………………………….<\/p>\n\n\n\n

Example in iris<\/h2>\n\n\n\n

\u9032\u5165python\u4e92\u52d5\u4ecb\u9762<\/strong>
# python<\/p>\n\n\n\n


\u8f09\u5165\u8cc7\u6599<\/strong>
>>> iris_X = iris.data
>>> iris_Y = iris.target
\u628a\u8cc7\u6599\u6253\u4e82
>>> np.random.seed(0)
>>> indices = np.random.permutation(len(iris_X))
\u9664\u4e86\u6700\u5f8c10\u7b46\u5916,\u90fd\u7d66training\u4f7f\u7528
>>> iris_X_train = iris_X[indices[:-10]]
>>> iris_Y_train = iris_Y[indices[:-10]]
\u5c07\u6700\u5f8c10\u7b46\u7d66validation\u4f7f\u7528
>>> iris_X_test = iris_X[indices[-10:]]
>>> iris_Y_test = iris_Y[indices[-10:]]<\/p>\n\n\n\n

\u8dd1training\u5efa\u7acbmodel<\/strong>
>>> from sklearn import linear_model
>>> logistic = linear_model.LogisticRegression(C=1e5)
>>> logistic.fit(iris_X_train,iris_Y_train)
LogisticRegression(C=100000.0, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class=’ovr’, penalty=’l2′, random_state=None,
solver=’liblinear’, tol=0.0001, verbose=0)<\/p>\n\n\n\n


\u6839\u64damodel\u9810\u6e2c\u8cc7\u6599<\/strong>
predict\u6703\u9078\u6a5f\u7387\u6700\u5927\u7684\u985e\u5225
>>> logistic.predict(iris_X_test)
array([1, 2, 1, 0, 0, 0, 2, 1, 2, 0])
>>> print(iris_Y_test)
[1 1 1 0 0 0 2 1 2 0]
\u4e0d\u904e\u9084\u662f\u6709\u4e00\u7b46\u9810\u6e2c\u932f\u8aa4<\/p>\n\n\n\n

\u5f9epredict_proba\u770b\u51fa\u5404\u985e\u5225\u5c0d\u61c9\u7684\u6a5f\u7387<\/strong>
>>> logistic.predict_proba(iris_X_test)
array([[ 7.84463971e-06, 9.99954368e-01, 3.77870449e-05],
[ 7.27268437e-09, 1.47166349e-01, 8.52833644e-01],
[ 2.71292961e-08, 9.99764511e-01, 2.35461414e-04],
[ 9.30834892e-01, 6.91651082e-02, 6.24975648e-25],
[ 9.67856008e-01, 3.21439919e-02, 2.01259183e-22],
[ 7.62068769e-01, 2.37931231e-01, 2.12976894e-23],
[ 3.61723169e-12, 3.29818401e-01, 6.70181598e-01],
[ 3.46987440e-06, 9.99996344e-01, 1.85665512e-07],
[ 1.40350469e-13, 7.03733483e-02, 9.29626652e-01],
[ 8.40655049e-01, 1.59344951e-01, 9.99274296e-23]])<\/p>\n\n\n\n

refer
Logistic Regression 3-class Classifier
http:\/\/scikit-learn.org\/stable\/auto_examples\/linear_model\/plot_iris_logistic.html<\/p>\n","protected":false},"excerpt":{"rendered":"

Logistic Regression\u5c6c\u65bc\u5206\u985e\u6f14\u7b97\u6cd5\uff0c\u900f\u904er …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[13],"tags":[],"class_list":["post-465","post","type-post","status-publish","format-standard","hentry","category-dataanalysis"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/posts\/465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/comments?post=465"}],"version-history":[{"count":0,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/posts\/465\/revisions"}],"wp:attachment":[{"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/media?parent=465"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/categories?post=465"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/systw.net\/note\/wp-json\/wp\/v2\/tags?post=465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}