LDA(n_components = 2) + fit_transform return 1-dim matrix instead of 2-dim

Question

While applying some LDA on my Churn_Modelling.csv file, eveything goes well until the point where my X_train return (8000, 1) except of (8000, 2) as expected :

lda = LDA(n_components = 2)

X_train = lda.fit_transform(X_train, y_train)

X_train is before-hand "hot-encoded" and "feature scaled" as followed :

# 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)

While doing the same on an other .csv file I have no troubles... do you have any idea why ?

Thank you very very much for your help !

Answer 1

I think I have the answer but I would prefer to have confirmation if possible :-)

The maximal number of columns I can hope to obtain using transform. is n-1 so, in my case, 2 classes (True, False) yields maximally 1 column (n-1).

Am I right ? Thank you again.