pandas Scikit-learn - 多项逻辑回归的错误输入形状错误
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Scikit-learn - Bad input shape error on multinomial logistic regression
提问by ExtremistEnigma
I'm implementing a multinomial logistic regression model in Python using Scikit-learn. Here's my code:
我正在使用 Scikit-learn 在 Python 中实现多项逻辑回归模型。这是我的代码：
X = pd.concat([each for each in feature_cols], axis=1)
y = train[["<5", "5-6", "6-7", "7-8", "8-9", "9-10"]]
lm = LogisticRegression(multi_class='multinomial', solver='lbfgs')
lm.fit(X, y)
However, I'm getting ValueError: bad input shape (50184, 6)when it tries to execute the last line of code.
但是，ValueError: bad input shape (50184, 6)当它尝试执行最后一行代码时，我得到了。
Xis a DataFramewith 50184 rows, 7 columns. yalso has 50184 rows, but 6 columns.
X是DataFrame50184 行，7 列。y也有 50184 行，但有 6 列。
I ultimately want to predict in what bin (<5, 5-6, etc.) the outcome falls. All the independent and dependent variables used in this case are dummy columns which have a binary value of either 0 or 1. What am I missing?
我最终想预测结果落在哪个区间（<5、5-6 等）。在这种情况下使用的所有自变量和因变量都是虚拟列，它们的二进制值为 0 或 1。我错过了什么？
采纳答案by Stefan
The Logistic Regression 3-class Classifierexample illustrates how fitting LogisticRegressionuses a vector rather than a matrix input, in this case the targetvariable of the irisdataset, coded as values [0, 1, 2].
的Logistic回归3级分类器实施例说明如何装配LogisticRegression使用的载体，而不是一个矩阵输入，在这种情况下，target所述的可变iris的数据集，编码为值[0, 1, 2]。
To convert the dummy matrix to a series, you could multiply each column with a different integer, and then - assuming it's a pandas.DataFrame- just call .sum(axis=1)on the result. Something like:
要将虚拟矩阵转换为序列，您可以将每一列与不同的整数相乘，然后 - 假设它是一个pandas.DataFrame- 只需调用.sum(axis=1)结果。就像是：
for i, col in enumerate(y.columns.tolist(), 1):
y.loc[:, col] *= i
y = y.sum(axis=1)
