sklearn ValueError：每个样本X具有5个功能；期待4

问题描述

我正在尝试对“ a”进行逻辑回归。这就是代码的样子。我是python的新手，所以深入的说明将非常有帮助（是的，这是两天后的演示的科学怪人代码）。自从我将变量数从4更改为5以来，问题似乎出在我重新训练数据的方式上。

data = pd.read_csv("C:/Users/-----/Desktop/Report Data.csv")

data = data[["a","b","c","d","e","f"
             ]]

data.fillna(0,inplace=True)

print(data.head())

predict = "a"

X = np.array(data.drop([predict],1))
y = np.array(data[predict])
x_train,x_test,y_train,y_test = sklearn.model_selection.train_test_split(X,y,test_size=0.2)

best = 100
for _ in range(100):
    x_train,test_size=0.2)

    log = linear_model.LogisticRegression()

    log.fit(x_train,y_train)
    acc = log.score(x_test,y_test)
    print(acc)
    if acc > best:
        best = acc
        with open("FMVmodel.pickle","wb") as f:
            pickle.dump(log,f)


pickle_in = open("FMVmodel.pickle","rb")
linear = pickle.load(pickle_in)

predictions = linear.predict(x_test)
for x in range(len(predictions)):
    print(predictions[x],x_test[x],y_test[x])

错误看起来像这样：

Traceback (most recent call last):
  File "C:/Users/SchaferJ/PycharmProjects/Model/main.py",line 65,in <module>
    predictions = linear.predict(x_test)
  File "C:\Users\SchaferJ\Anaconda4\lib\site-packages\sklearn\linear_model\_base.py",line 307,in predict
    scores = self.decision_function(X)
  File "C:\Users\SchaferJ\Anaconda4\lib\site-packages\sklearn\linear_model\_base.py",line 286,in decision_function
    raise ValueError("X has %d features per sample; expecting %d"
ValueError: X has 5 features per sample; expecting 4

解决方法

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