訓練集中的訓練數據

Question

上午，嘗試使用scikit-learn在我的訓練集中訓練模型，但出現此錯誤：

 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.

步驟 1：將 x 和 y 拆分為訓練和測試集

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)

檢查新分割的 x 值的形狀（訓練和測試）

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

檢查新分割的 y 值的形狀（訓練和測試）

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

第 2 步：在訓練集上訓練我們的模型（使用邏輯回歸）

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

運行此代碼我收到錯誤

Answer 1

看來您正在用它們的形狀替換數據點：

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

刪除這些行並重新運行。

Answer 2

您做得很好，但您做錯了一件事：您將訓練和測試數據替換為一維形狀，這就是您面臨此錯誤的原因

 #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