[英]LDA(n_components = 2) + fit_transform return 1-dim matrix instead of 2-dim
在我的Churn_Modelling.csv文件上應用一些LDA時,一切順利,直到我的X_train返回(8000,1),除了(8000,2)之外,均達到預期:
lda = LDA(n_components = 2)
X_train = lda.fit_transform(X_train, y_train)
X_train預先經過“熱編碼”和“功能縮放”,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文件上執行相同操作時,我沒有遇到任何麻煩...您知道為什么嗎?
非常感謝您的幫助!
我想我有答案,但如果可能的話,我希望得到確認:-)
我希望使用transform可以獲得的最大列數。 是n-1,因此,在我的情況下,2個類(真,假)最多產生1列(n-1)。
我對嗎 ? 再次感謝你。
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