LDA（n_components = 2）+ fit_transform返回1维矩阵而不是2维矩阵

Question

在我的Churn_Modelling.csv文件上应用一些LDA时，一切顺利，直到我的X_train返回（8000，1），除了（8000，2）之外，均达到预期：

lda = LDA(n_components = 2)

X_train = lda.fit_transform(X_train, y_train)

X_train预先经过“热编码”和“功能缩放”，如下所示：

# LDA

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Applying LDA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components = 2)
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)

在其他.csv文件上执行相同操作时，我没有遇到任何麻烦...您知道为什么吗？

非常感谢您的帮助！

Answer 1

我想我有答案，但如果可能的话，我希望得到确认:-)

我希望使用transform可以获得的最大列数。 是n-1，因此，在我的情况下，2个类（真，假）最多产生1列（n-1）。

我对吗 ？ 再次感谢你。