The model in linear regression is $y = omega^T x + e$ , where x, y, e represent the feature, the target and the noise, respecively.
p(y >, omega)常常被称为线性回归模式的可能性。
In naive Bayesian algorithm, p(x|y) is called likelihood function.
这两种可能性功能混淆不清,因为X和x的顺序正相反。 如何解释这两个可能性? 感谢。
In the logistic regression model, p(y|x, omega) is termed conditional likelihood function, in which omega is the parameter.
在Navive Bayesian族分类器中,第(x)页是特征的可能功能,因为在这种情况下,标签是我们能够改变的。 在纳维德湾,我们注重不同阶层的特征分布。
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