mirror
/
learn-python
kopia lustrzana https://github.com/animator/learn-python
1
0
Forkuj 0
Kod Zgłoszenia Packages Projekty Wydania Wiki Aktywność

Update Naive_Bayes_Classifiers.md

pull/1210/head
Divyanshi 2024-06-19 14:56:35 +05:30 zatwierdzone przez GitHub
rodzic bab7d667e6
commit 10c5fb5bc2
Nie znaleziono w bazie danych klucza dla tego podpisu
ID klucza GPG: B5690EEEBB952194
1 zmienionych plików z 4 dodań i 3 usunięć
Pokaż statystyki Ściągnij plik aktualizacji Ściągnij plik porównania Expand all files Collapse all files

7
contrib/machine-learning/Naive_Bayes_Classifiers.md
Wyświetl plik

@ -8,9 +8,6 @@ Bayes’ Theorem finds the probability of an event occurring given the probabili
$$ P(C|X) = \frac{P(X|C) \cdot P(C)}{P(X)} $$ $$ P(C|X) = \frac{P(X|C) \cdot P(C)}{P(X)} $$
![img.png](img.png)
where A and B are events and P(B) ≠ 0 where A and B are events and P(B) ≠ 0
* We are trying to find probability of event A, given the event B is true. * We are trying to find probability of event A, given the event B is true.
@ -78,6 +75,10 @@ In Gaussian Naive Bayes, continuous values associated with each feature are assu
* Assumption: Each feature follows a Gaussian distribution. * Assumption: Each feature follows a Gaussian distribution.
* Formula: The likelihood of the features given the class is computed using the Gaussian (normal) distribution formula: * Formula: The likelihood of the features given the class is computed using the Gaussian (normal) distribution formula:
$$
P(C_k | x) = \frac{P(C_k) \cdot \prod_{i=1}^{n} \frac{1}{\sqrt{2 \pi \sigma_{k,i}^2}} \exp \left( -\frac{(x_i - \mu_{k,i})^2}{2 \sigma_{k,i}^2} \right)}{P(x)}
$$
![img_3.png](img_3.png) ![img_3.png](img_3.png)
where 𝜇𝐶 and 𝜎𝐶 are the mean and standard deviation of the feature 𝑥𝑖 for class C. where 𝜇𝐶 and 𝜎𝐶 are the mean and standard deviation of the feature 𝑥𝑖 for class C.