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Learn the theory, math and code behind different machine learning algorithms and techniques.

Home Page: https://ml-explained.com/

License: MIT License

Jupyter Notebook 27.47% Python 72.53%

machine-learning machine-learning-algorithms math machine-learning-theory

machine-learning-explained's Introduction

Hi 👋, I'm Gilbert Tanner

I'm a software engineer from Austria currently studying Robotics and Artificial Intelligence at the University of Klagenfurt.

👨🏽‍💻 Currently working and growing my skillset in Robotics and Artificial Intelligence
🤝 Open for collaborations regarding technical articles and Machine Learning projects
🌐 Visit my website for more information

📕 Latest Blog Posts

🤝Connect with me:

machine-learning-explained's People

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momodevlop ajaypayattuparambil buzzsuresh anhnguyendepocen avinogra shihaocui houssamezzin aungmyatsan ssensalo nibiruft kaix90 jiayh-sustech shamodyahashantha satbina anilgk81 prestasicode aserehal hewramanwaran fismoilov

machine-learning-explained's Issues

Bug?

Hello, I think there might be a bug in your linear_discriminant_analysis.py script
You are computing

Between class scatter matrix

    total_mean = np.mean(X, axis=0)
    S_B = np.empty((n_features, n_features))
    for label in labels:
        _X = X[y == label]
        _mean = np.mean(_X, axis=0)
        S_B += len(_X) * (_mean - total_mean).dot((_mean - total_mean).T)

Which gives a NxN S_B matrix, where N is the number of features. But with

_mean = np.mean(_X, axis=0)

the _mean has shape (N, ), which makes (_mean - total_mean).dot((_mean - total_mean).T) a scalar instead of a matrix. S_B keeps to have correct shape because of its initialization, but incorrect values.

I might be wrong, but please consider this.

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