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  1. 123

    Linear Discriminant Analysis (LDA) can be used not only for classification but also to assess feature importance by examining the model’s coefficients. In scikit-learn, the coef_ attribute of a fitted LinearDiscriminantAnalysis model provides the weight of each feature in separating classes, which can be interpreted as a measure of importance.

    Steps to Implement

    1. Import Libraries and Load Data

    import numpy as np
    import pandas as pd
    from sklearn.datasets import load_iris
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

    # Load dataset
    iris = load_iris()
    X = iris.data
    y = iris.target
    feature_names = iris.feature_names
    Copied!

    Here, we use the Iris dataset for demonstration, but you can replace it with your own data.

    2. Fit the LDA Model

    lda = LinearDiscriminantAnalysis()
    lda.fit(X, y)
    Copied!

    This step trains the LDA model to find linear combinations of features that best separate the classes.

    3. Extract and Rank Feature Importance

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  2. Statology
    https://www.statology.org › linear-discriminant-analysis-in-python

    Linear Discriminant Analysis in Python (Step-by-Step) - Statology

    This tutorial explains how to perform linear discriminant analysis in Python, including a step-by-step example.
    Step 2: Load The Data

    For this example, we’ll use the irisdataset from the sklearn library. The following code shows how to load this dataset and convert it to a pandas DataFrame to make it easy to work with: We can see that the dataset contains 150 total observations. For this e…

    Step 3: Fit The Lda Model

    Next, we’ll fit the LDA model to our data using the LinearDiscriminantAnalsyisfunction from sklearn:

    Step 4: Use The Model to Make Predictions

    Once we’ve fit the model using our data, we can evaluate how well the model performed by using repeated stratified k-fold cross validation. For this example, we’ll use 10 folds and 3 repeats: We can see that the model performed a mean accuracy of 97.…

  3. GeeksForGeeks
    https://www.geeksforgeeks.org › machine-learning › ml...

    Linear Discriminant Analysis in Machine Learning

    • See More

    Sep 13, 2025 · Linear Discriminant Analysis (LDA) also known as Normal Discriminant Analysis is supervised classification problem that helps separate two or more classes by converting higher …

  5. scikit-learn
    https://scikit-learn.org › stable › modules › generated...

    LinearDiscriminantAnalysis — scikit-learn 1.8.0 documentation

    Linear Discriminant Analysis. A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule. The model fits a Gaussian density to each …

  6. Machine Learning Mastery
    https://machinelearningmastery.com › linear-discrimi...

    Linear Discriminant Analysis With Python - Machine …

    Aug 3, 2020 · In this tutorial, you will discover the Linear Discriminant Analysis classification machine learning algorithm in Python. After completing this …

  7. IBM Developer
    https://developer.ibm.com › tutorials › awb...

    Implementing linear discriminant analysis (LDA) in Python

    Mar 17, 2024 · In this Python tutorial, we delve deeper into LDA with Python, implementing LDA to optimize a machine learning model's performance by using …

  8. Stack Abuse
    https://stackabuse.com › implementing-lda-in-python...

    Linear Discriminant Analysis (LDA) in Python with Scikit …

    Nov 16, 2023 · Let us now see how we can implement LDA using Python's Scikit-Learn. Like PCA, the Scikit-Learn library contains built-in classes for performing …

  9. aicodesnippet.com
    https://www.aicodesnippet.com › machine-learning › ...

    Linear Discriminant Analysis (LDA) Explained with Python Examples

    We will explore the underlying principles of LDA, its advantages and disadvantages, and demonstrate its implementation in Python with scikit-learn. Through code examples and explanations, you'll learn how …

  10. pythonprog.com
    https://www.pythonprog.com › linear-discriminant...

    Linear Discriminant Analysis (LDA) in Machine Learning (python scikit ...

    Sep 14, 2023 · Python: Familiarity with the Python programming language and its scientific computing libraries such as NumPy, Pandas, and Scikit-learn is important as LDA is commonly implemented …

  11. Towards Data Science
    https://towardsdatascience.com › lda-linear-discrimi...

    LDA: Linear Discriminant Analysis - How to Improve Your …

    Aug 8, 2021 · In this article, I give an intuitive explanation of how LDA works while highlighting the differences to PCA. At the same time, I provide a Python …

  12. Github
    https://github.com › xbeat › Machine-Learning › blob › main...

    Machine-Learning/Building a Linear Discriminant …

    To assess the performance of our LDA implementation, we can split our data into training and testing sets, train the LDA on the training data, and evaluate its …

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