【问题标题】：Training data in a training set训练集中的训练数据
【发布时间】：2020-01-31 14:46:48
【问题描述】：

我正在尝试使用 scikit-learn 在我的训练集中训练一个模型，但出现此错误：

 ValueError: Expected 2D array, got 1D array instead: array=[90.  4.].
 Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

第 1 步：将 x 和 y 拆分为训练和测试集

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.4, random_state = 4)

检查新拆分的 x 值的形状（训练和测试）

X_train = X_train.shape
X_test = X_test.shape
print(X_train)
print(X_test)

检查新分割的 y 值的形状（训练和测试）

y_train = y_train.shape
y_test = y_test.shape
print(y_train)
print(y_test)

第 2 步：在训练集上训练我们的模型（使用逻辑回归）

logR = LogisticRegression()
logR = logR.fit(X_train, y_train)

运行这段代码我得到了错误

【问题讨论】：

标签： python scikit-learn data-science

【解决方案1】：

您似乎正在用它们的形状替换数据点：

X_train = X_train.shape
X_test = X_test.shape
y_train = y_train.shape
y_test = y_test.shape

删除这些行并重新运行。

【讨论】：

【解决方案2】：

您做得很好，但您做错了一件事：您将训练和测试数据替换为 1D 形状，这就是您面临此错误的原因

 #replace these line

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.4, random_state = 4)

print( X_train.shape)
print( X_test.shape)
print(y_train.shape)
print(y_test.shape)

logR = LogisticRegression()
logR = logR.fit(X_train, y_train)

# Now it work fine

【讨论】：