Jussi Jousimo statguy
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| Based on Damien Benveniste, PhD 'quick Feature Selection' method | |
| original post: https://lnkd.in/gCDSEJcF | |
| quick FEATURE SELECTION | |
| train a Supervised Learning algorithm with a Feature Importance measure | |
| This is also a method that can be used for highly non-linear data as opposed to LASSO (for example) that tends to only understand linear relationships in the data. The random feature is a "Random Bar" because this is the minimum bar a feature needs to beat to be a part of the potentially useful features set. Now it doesn't mean there are not additional features that could be beneficial to further remove to optimize your model. | |
| This is a technique I like to perform a quick FEATURE SELECTION for Machine Learning applications. I tend to call it the "Random Bar" method! Let's assume you have a feature set X and a target Y. Let's create a random vector V (for example np.random.normal(size=(1, 100))) and append that vector as a new feature to X: |