machine-learning - ML分类中的SMOTE

我正在使用sklearn在Jupyter中运行分类算法。
我想使用SMOTE，因为我的一个小组只有其他2个小组的35％。因此，我想对该组（第1组）进行超采样，但我不知道如何对其进行集成。 （编辑：我知道SMOTE脚本，但是我想知道它在下面的脚本中适合的位置）。
救命？

from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split (X, y, test_size = 0.1)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform (X_test)
from sklearn.svm import SVC
clf = SVC(kernel = 'linear')
clf.fit (X_train, y_train.ravel())
y_pred = clf.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix (y_test, y_pred)
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score (estimator = clf, X = X_train, y = y_train, cv = 10)
accuracies.mean()
accuracies.std()
from sklearn.model_selection import GridSearchCV
parameters = [{'C':[1, 10, 100], 'kernel':['linear']},
              {'C':[1, 10, 100],
               'kernel':['rbf'],
               'gamma': [0.05, 0.001,　0.005]}]
grid_search = GridSearchCV (estimator = clf, param_grid = parameters, scoring = 'accuracy', cv = 10)
grid_search = grid_search.fit (X_train,y_train)
best_accuracy =  grid_search.best_score_
print (best_accuracy)
best_parameters = grid_search.best_params_
print (best_parameters)

最佳答案

您必须对数据集进行SMOTE，并使用生成的平衡数据集进行建模训练。

因此，您必须像问题中未显示的代码那样加载数据，然后对其应用SMOTE。

就代码而言，可以这样完成

X = # train data
y = # train labels

# applying SMOTE

from imblearn.over_sampling import SMOTE

sm = SMOTE(random_state=42)
X_balanced, y_balanced = sm.fit_sample(X, y)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split (X_balanced, y_balanced, test_size = 0.1)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform (X_test)
from sklearn.svm import SVC
clf = SVC(kernel = 'linear')
clf.fit (X_train, y_train.ravel())
y_pred = clf.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix (y_test, y_pred)
from sklearn.model_selection import cross_val_score
accuracies = cross_val_score (estimator = clf, X = X_train, y = y_train, cv = 10)
accuracies.mean()
accuracies.std()
from sklearn.model_selection import GridSearchCV
parameters = [{'C':[1, 10, 100], 'kernel':['linear']},
              {'C':[1, 10, 100],
               'kernel':['rbf'],
               'gamma': [0.05, 0.001,　0.005]}]
grid_search = GridSearchCV (estimator = clf, param_grid = parameters, scoring = 'accuracy', cv = 10)
grid_search = grid_search.fit (X_train,y_train)
best_accuracy =  grid_search.best_score_
print (best_accuracy)
best_parameters = grid_search.best_params_
print (best_parameters)

关于machine-learning - ML分类中的SMOTE，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/51299224/