python - sklearn选择Kbest : which variables were chosen?

Question

我正在尝试让 sklearn 为线性回归选择最佳的 k 个变量(例如 k=1)。这行得通，我可以获得 R 平方，但它没有告诉我哪些变量是最好的。我怎样才能找到它？

我有以下形式的代码(真正的变量列表要长得多):

X=[]
for i in range(len(df)):
X.append([averageindegree[i],indeg3_sum[i],indeg5_sum[i],indeg10_sum[i])


training=[]
actual=[]
counter=0
for fold in range(500):
    X_train, X_test, y_train, y_test = crossval.train_test_split(X, y, test_size=0.3)
    clf = LinearRegression()
    #clf = RidgeCV()
    #clf = LogisticRegression()
    #clf=ElasticNetCV()

    b = fs.SelectKBest(fs.f_regression, k=1) #k is number of features.
    b.fit(X_train, y_train)
    #print b.get_params

    X_train = X_train[:, b.get_support()]
    X_test = X_test[:, b.get_support()]


    clf.fit(X_train,y_train)
    sc = clf.score(X_train, y_train)
    training.append(sc)
    #print "The training R-Squared for fold " + str(1) + " is " + str(round(sc*100,1))+"%"
    sc = clf.score(X_test, y_test)
    actual.append(sc)
    #print "The actual R-Squared for fold " + str(1) + " is " + str(round(sc*100,1))+"%"

Answer 1

您需要使用 get_support:

features_columns = [.......]
fs = SelectKBest(score_func=f_regression, k=5)
print zip(fs.get_support(),features_columns)