Gaussian

這是給自己的一份學習紀錄，以免日子久了忘記這是甚麼理論XD

這是給自己的一份學習紀錄，以免日子久了忘記這是甚麼理論XD 👶 Naive Bayes By definition of Bayes’ theorem $$ P(y \mid x_1, x_2, …, x_n) = \frac{P(y)P(x_1, x_2, …, x_n \mid y)}{P(x_1, x_2, …, x_n)} $$ where $P(y)$ represents the prior probability of class $y$ $P(x_1, x_2, …, x_n \mid y)$ represents the likelihood, i.e., the probability of observing features $x_1, x_2, …, x_n$ given class $y$ $P(x_1, x_2, …, x_n)$ represents the marginal probability of the feature set $x_1, x_2, …, x_n$ With the assumption of Naive Bayes - Conditional Independence $$ P(x_i \mid y, x_1, …, x_{i-1}, x_{i+1}, …, x_n) = P(x_i \mid y) $$ ...