来看看 random_state 这个参数 SVC(random_state=0)里有参数 random_state from imblearn.over_sampling import SMOTE SMOTE(random_state=42) 里有参数 random_state 上面一个是svd算法,一个是处理不平衡数据的smote算法,我都遇到了random_state这个参数,那么......
...机分割训练集和测试集: # test_size:设置测试集的比例。random_state:可理解为种子,保证随机唯一 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3., random_state=8) sklearn实战例子: from sklearn import datasets ...
...机分割训练集和测试集: # test_size:设置测试集的比例。random_state:可理解为种子,保证随机唯一 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3., random_state=8) sklearn实战例子: from sklearn import datasets ...
...Frame # 生成2分类数据集 X, y = make_moons(n_samples=100, noise=0.2, random_state=1) print(X.shape) print(X[:6]) print(y.shape) print(y[:6]) df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y)) colors = {0:...
... =cross_validation.train_test_split(train_data,train_target,test_size=0.3, random_state=0) 参数解释: train_data:被划分的样本特征集 train_target:被划分的样本标签 test_size:如果是浮点数,在0-1之间,表示样本占比;如果是整数的话就是样本的数...
...集占比x_train,x_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=90)lr=LogisticRegression(max_iter=3000)clm=lr.fit(x_train,y_train)print(对测试集的预测结果:)#输出预测结果、预测结果的结构类型及尺寸result=clm.p...
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