ML&DL：ML&DL相关概念的原始英文解释——对理解最初的ML和DL的相关概念的定义非常有用
目录
ML分类概念
1、Parametric and Nonparametric Algorithms
ML分类概念 1、Parametric and Nonparametric Algorithms
What is a parametric machine learning algorithm and how is it different from a nonparametric machine learning algorithm?
1、Assumptions can greatly simplify the learning process, but can also limit what can be learned. Algorithms that simplify the function to a known form are called parametric machine learning algorithms. (1)、The algorithms involve two steps: A、Select a form for the function. B、Learn the coefficients for the function from the training data. Some examples of parametric machine learning algorithms are Linear Regression and Logistic Regression.
2、Algorithms that do not make strong assumptions about the form of the mapping function are called nonparametric machine learning algorithms. By not making assumptions, they are free to learn any functional form from the training data. (1)、Non-parametric methods are often more flexible, achieve better accuracy but require a lot more data and training time. (2)、Examples of nonparametric algorithms include Support Vector Machines, Neural Networks and Decision Trees.
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