random_state=1234)# test if elasticnet with l1_ratio near 1 gives same result as pure l1est_en =SGDClassifier(alpha=0.001, penalty='elasticnet', tol=None, max_iter=6, l1_ratio=0.9999999999, random_state=42).fit(X, y) est_l1 =SGDClassifier(alpha=0.001, penalty='l1', max_iter=6, r...
y =data.target X_train, X_test, y_train, y_test = train_test_split(x, y, test_size =0.3, random_state =42) EN = SGDClassifier(loss='log', penalty='elasticnet', alpha=0.0001, l1_ratio=0.15) EN.fit(X_train, y_train) numpy.all(EN.predict(X_test) == EN.predict_proba(X_tes...
data_train_tfidf=tfidf_transformer.fit_transform(X_train_counts) clf_svm=SGDClassifier(loss='log',penalty='l2',alpha=1e-3,n_iter=5,random_state=42).fit(data_train_tfidf,data_train_loc.ravel()) X_new_counts=count_vect.transform(data_test.ravel()) X_new_tfidf=tfidf_transformer.trans...
print("转变完了") sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42) #将数字标签转换为bool型标签,List内item的转换 y_train_5 = (y_train == 5) print("训练中") sgd_clf.fit(x_train_transed,y_train_5) print("训练完了") print("正在保存模型") with open('./5_...
sklearn.linear_model.SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, class_weight=None, warm...
import numpy as np import matplotlib.pyplot as plt from sklearn import datasets x, y = datasets.make_moons(n_samples=500, noise=.3, random_state=42) #生成数据集 print(x) #数据集可视化 plt.scatter(x[y... sklearn随机森林-分类参数详解 ...