Date
Oblique decision treeAlso known as multivariate decision tree, it uses the linear expression of multiple attributes of the node as the evaluation criterion. Compared with the univariate decision tree, it has the ability to model the complex relationship between continuous attributes.
Each node of a univariate decision tree uses the same attribute. If the generated decision tree is represented in a coordinate space (the attribute is the coordinate axis), then the boundaries are parallel to the coordinate axis. However, a single attribute is difficult to characterize the classification edge in some cases, thus causing jitter. However, this jitter can be solved with just one hypotenuse, which is the linear combination of the attributes.
【1】https://blog.csdn.net/u012882134/article/details/78305766
【2】http://www.wangxianfeng.name/2011/08/characteristic-of-decision-tree/
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.
Date
Oblique decision treeAlso known as multivariate decision tree, it uses the linear expression of multiple attributes of the node as the evaluation criterion. Compared with the univariate decision tree, it has the ability to model the complex relationship between continuous attributes.
Each node of a univariate decision tree uses the same attribute. If the generated decision tree is represented in a coordinate space (the attribute is the coordinate axis), then the boundaries are parallel to the coordinate axis. However, a single attribute is difficult to characterize the classification edge in some cases, thus causing jitter. However, this jitter can be solved with just one hypotenuse, which is the linear combination of the attributes.
【1】https://blog.csdn.net/u012882134/article/details/78305766
【2】http://www.wangxianfeng.name/2011/08/characteristic-of-decision-tree/
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.