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Update feature_subset_selection.ipynb #404

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May 23, 2020
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Update feature_subset_selection.ipynb #404

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Update feature_subset_selection.ipynb
diegoroman17 May 20, 2020
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Update feature_subset_selection.ipynb
diegoroman17 May 22, 2020
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2 changes: 1 addition & 1 deletion docs/examples/usecases/feature_subset_selection.ipynb
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Expand Up @@ -38,7 +38,7 @@
"\n",
"Then this means that the second, third, and fourth (or first, second, and third in zero-index) that are turned on are the selected features for the dataset. We can then train our classifier using only these features while dropping the others. How do we then define our objective function? (Yes, another rhetorical question!). We can design our own, but for now I'll be taking an equation from the works of [Vieira, Mendoca, Sousa, et al. (2013)](http://www.sciencedirect.com/science/article/pii/S1568494613001361).\n",
"\n",
"$$f(X) = \\alpha(1-P) + (1-\\alpha) \\left(1 - \\dfrac{N_f}{N_t}\\right)$$\n",
"$$f(X) = \\alpha(1-P) + (1-\\alpha) \\left(\\dfrac{N_f}{N_t}\\right)$$\n",
"\n",
"Where $\\alpha$ is a hyperparameter that decides the tradeoff between the classifier performance $P$, and the size of the feature subset $N_f$ with respect to the total number of features $N_t$. The classifier performance can be the accuracy, F-score, precision, and so on."
]
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