高斯分布(Gaussian distribution) 又名正态分布(Normal distribution)/常态分布. 是一个在数学物理及工程等领域都非常重要的概率分布, 在统计学的许多方面有着重大的影响力, 正态曲线呈钟型, 若随机变量X服从一个数学期望为μ, 方差为σ^2的正态分布, 记为N(μ,σ^2). 其概率密度函数为正态分布的期望值μ...
3. Naive Bayes Classifier 朴素贝叶斯是另一种概率生成模型,基于条件独立性假设,可看作一种最简单的概率有向图模型。 变量x的各个维度在给定y的条件下是相互独立的。这个假设虽然不一定合理,但却能大大简化运算,这就是朴素贝叶斯的目的。 基于条件独立性假设,可以有: P(x∣y)=∏j=1pP(xj∣y)y^=argmaxy...
Naïve Bayes means every features are independent \frac{p(x_{1:N}|y_{1:N},\eta)p(\eta)}{p(x_{1:N}|y_{1:N})}=\prod_{j=1}^D\prod_{c=1}^C\frac{p(\eta_{jc})p(x_{i\in c,j}|\eta_{jc})}{p(x_{i\in c,j})}\\ =\prod_{j=1}^D\prod_{c=1}^Cp(\eta...
高斯分布(Gaussian distribution) 又名正态分布(Normal distribution)/常态分布. 是一个在数学物理及工程等领域都非常重要的概率分布, 在统计学的许多方面有着重大的影响力, 正态曲线呈钟型, 若随机变量X服从一个数学期望为μ, 方差为σ^2的正态分布, 记为N(μ,σ^2). 其概率密度函数为正态分布的期望值μ...
Normal (Gaussian) Distribution The'normal'distribution (specify using'normal') is appropriate for predictors that have normal distributions in each class. For each predictor you model with a normal distribution, the naive Bayes classifier estimates a separate normal distribution for each class by comput...
Gaussian Naive Bayes classifier In Gaussian Naive Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution. A Gaussian distribution is also called Normal distribution. It gives a bell shaped curve when plotted, which is symmetrical with th...
6.Mixed NB(Gaussian + Categorical)approach 2— train two separate models using continuous and categorical variables and then train the final model based on predictions from the first two models What category of algorithms does the Naive Bayes classifier belong to?
Implementing a Naive Bayes Classifier from Scratch Create a function that calculates the prior probability, P(H), mean and variance of each class. The mean and variance are later used to calculate the likelihood, P(E|H), using the Gaussian distribution. ...
While the code is quite short it is still too long to be completely sure that we didn’t do any mistakes. So, let us check how it fares against thescikit-learn GaussianNB classifier. my_gauss = GaussianNaiveBayesClassifier() my_gauss.fit(X, y) my_gauss.predict_proba([[-2, 5], [...
We extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and ...