LDA is a dimensionality reduction technique commonly used in the preprocessing step for pattern classification tasks.
The main goal is to project a dataset onto a lower-dimensional space while maximizing the separation between multiple classes.
Key Features:
Similarity to PCA:
Both LDA and PCA are used for dimensionality reduction.
Both aim to project data into a lower-dimensional space.
Differences from PCA:
PCA (Principal Component Analysis):
Focuses on finding components that capture the most variance in the dataset.
Unsupervised: Does not consider the relationship to the dependent variable.
LDA (Linear Discriminant Analysis):
Focuses on finding the axes that maximize the separation between different classes.
Supervised: Uses class labels (dependent variable, y) to guide the projection.
Steps in LDA:
Apply Feature Scaling: Ensure features are scaled before applying LDA to prevent bias from features with larger ranges.
Include Dependent Variable (y):
Unlike PCA, which works solely on the independent variables (X_train), LDA incorporates the dependent variable (y_train) to account for class separation.
Project Data:
LDA identifies the linear discriminants (axes) that maximize the separation between classes.
Code Example (Using Scikit-Learn):
python
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
# Initialize LDA
lda = LDA()
# Fit and transform the training data (include both X_train and y_train)
X_train_lda = lda.fit_transform(X_train, y_train)
Note: The key difference in LDA is that it takes both X_train (independent variables) and y_train (dependent variable) during training, making it a supervised technique.
Overview:
Key Features:
Steps in LDA:
Code Example (Using Scikit-Learn):