python - 拟合LogisticRegression时发现输入变量样本数量不一致

Question

我正在创建LogisticRegression分类器代码如下:

regressor = LogisticRegression()
regressor.fit(x_train, y_train)

x_train 和 y_train 形状都是

<class 'tuple'>: (32383,)

x_train 包含范围 [0..1] 附近的值，而 y_train 仅包含 0 且1秒。

不幸的是，fit 失败并出现错误

ValueError: Found input variables with inconsistent numbers of samples: [1, 32383]

向参数添加转置没有帮助。

Answer 1

继续我在评论中提出的解决方案:问题在于 x_train 的形状。所以我们需要 reshape 它:

来自文档:

X:{类似数组，稀疏矩阵}，形状(n_samples，n_features)

y:类似数组，形状 (n_samples,)

示例使用scikit-learn和numpy:

from sklearn.linear_model import LogisticRegression
import numpy as np

# create the tuple data
x_train = tuple(range(32383))
x_train = np.asarray(x_train)

#same for y_train
y_train=tuple(range(32383))
y_train = np.asarray(y_train)

#convert tuples to nparray and reshape the x_train
x_train = x_train.reshape(32383,1)

#check if shape if (32383,)
y_train.shape

#create the model
lg = LogisticRegression()

#Fit the model
lg.fit(x_train, y_train)

这应该可以正常工作。希望对您有帮助