Linear Discriminant Analysis Clearly Explained

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Introduction of Linear Discriminant Analysis Clearly Explained

Linear Discriminant Analysis (LDA) is a supervised dimensionality reduction technique that projects data in a way that maximizes class separation. Unlike PCA, which is unsupervised and focuses on variance, LDA leverages class labels to identify directions that best distinguish categories. In a financial context, LDA was applied to a synthetic dataset of 1,000 borrowers to predict loan default based on features like credit score, loan amount, interest rate, and income. A logistic function was used to create the default target variable, and features were standardized before applying LDA. The data was reduced to a single linear discriminant (LD1), and visualized using histograms to show separation between default and non-default borrowers. A logistic regression classifier trained on LD1 achieved 99% accuracy overall, although recall for defaulters remained low due to class imbalance. The feature importance from LDA revealed that loan amount, interest rate, and debt-to-income ratio contributed positively to default, while income and credit score had negative influence. A horizontal bar chart illustrated these coefficients and their directional effects. LDA’s strength lies in its ability to not just reduce dimensions, but to do so in a way that improves classification. It can also be combined with PCA or SVM in a workflow to enhance performance and interpretability.

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Linear Discriminant Analysis Clearly Explained

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Linear Discriminant Analysis (LDA) is a supervised dimensionality reduction technique that projects data in a way that maximizes class separation. Unlike PCA, which is unsupervised and focuses on variance, LDA leverages class labels to identify directions that best distinguish categories. In a financial context, LDA was applied to a synthetic dataset of 1,000 borrowers to predict loan default based on features like credit score, loan amount, interest rate, and income. A logistic function was used to create the default target variable, and features were standardized before applying LDA. The data was reduced to a single linear discriminant (LD1), and visualized using histograms to show separation between default and non-default borrowers. A logistic regression classifier trained on LD1 achieved 99% accuracy overall, although recall for defaulters remained low due to class imbalance. The feature importance from LDA revealed that loan amount, interest rate, and debt-to-income ratio contributed positively to default, while income and credit score had negative influence. A horizontal bar chart illustrated these coefficients and their directional effects. LDA’s strength lies in its ability to not just reduce dimensions, but to do so in a way that improves classification. It can also be combined with PCA or SVM in a workflow to enhance performance and interpretability.

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Last Updated: May 22, 2026

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