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Typo correction. #80

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2 changes: 1 addition & 1 deletion notebooks/05.05-Naive-Bayes.ipynb
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Expand Up @@ -268,7 +268,7 @@
"Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution.\n",
"The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates.\n",
"\n",
"The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution."
"The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribution with a best-fit multinomial distribution."
]
},
{
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