参数和非参数方法之间的区别

参数方法
参数方法背后的基本思想是，有一组固定参数用于确定机器学习中使用的概率模型。
参数方法是那些我们事先知道总体是正态的方法，或者如果不是，那么我们可以使用正态分布轻松地近似它，这可以通过调用中心极限定理来实现。
使用正态分布的参数是 –

• 意思
• 标准差

最终，参数化方法的分类完全取决于对总体所做的假设。有许多可用的参数方法，其中一些是：

• 用于 – 总体均值和已知标准差的置信区间。
• 置信区间用于 – 总体均值以及未知标准差。
• 总体方差的置信区间。
• 两个均值之差的置信区间，标准差未知。

非参数方法
在非参数方法中，不需要对给定的总体或我们正在研究的总体进行任何参数假设。事实上，这些方法并不取决于人口。
这里没有可用的固定参数集，也没有任何类型的分布（正态分布等）可供使用。这也是非参数方法也称为无分布方法的原因。
如今，非参数方法越来越受欢迎，而这种名声背后的一些原因是——

• 主要的原因是使用参数化方法时不需要有礼貌。
• 第二个重要原因是，我们不需要对我们正在研究的给定（或采用）总体做出越来越多的假设。
• 大多数可用的非参数方法都非常容易应用和理解，即复杂性非常低。

今天有许多非参数方法可用，但其中一些是——

• 斯皮尔曼相关性检验
• 总体均值的符号检验
• 两个独立均值的 U 检验

参数和非参数方法之间的区别如下 -

S.No.	Parametric Methods	Non-Parametric Methods
1.	Parametric Methods uses a fixed number of parameters to build the model.	Non-Parametric Methods use the flexible number of parameters to build the model.
2.	Parametric analysis is to test group means.	A non-parametric analysis is to test medians.
3.	It is applicable only for variables.	It is applicable for both – Variable and Attribute.
4.	It always considers strong assumptions about data.	It generally fewer assumptions about data.
5.	Parametric Methods require lesser data than Non-Parametric Methods.	Non-Parametric Methods requires much more data than Parametric Methods.
6.	Parametric methods assumed to be a normal distribution.	There is no assumed distribution in non-parametric methods.
7.	Parametric data handles – Intervals data or ratio data.	But non-parametric methods handle original data.
8.	Here when we use parametric methods then the result or outputs generated can be easily affected by outliers.	When we use non-parametric methods then the result or outputs generated cannot be seriously affected by outliers.
9.	Parametric Methods can perform well in many situations but its performance is at peak (top) when the spread of each group is different.	Similarly, Non-Parametric Methods can perform well in many situations but its performance is at peak (top) when the spread of each group is the same.
10.	Parametric methods have more statistical power than Non-Parametric methods.	Non-parametric methods have less statistical power than Parametric methods.
11.	As far as the computation is considered these methods are computationally faster than the Non-Parametric methods.	As far as the computation is considered these methods are computationally faster than the Parametric methods.
12.	Examples – Logistic Regression, Naïve Bayes Model, etc.	Examples – KNN, Decision Tree Model, etc.