You just finished training a decision tree for spam classification, and it is gettingabnormally bad performance on both your training and test sets. You know that yourimplementation has no bugs, so what could be causing the problem?

(a) Your decision trees are too shallow.
(b) You need to increase the learning rate.
(c) You are overfitting.
(d) All of the above

The following answer is true because when the person completed our training of a decision tree and after the following presentation he getting not good working performance on both side i.e., test sets and during the training period. After the training there is no bug on the implementation of the presentation then, he has to increase the rate of the learning.

Answer Link

Martebi Martebi · Answer 2 · 2022-03-21T15:04:51+01:00

The option that is causing the bad performance problem is that your decision trees are too shallow.

What is a decision tree?

A decision tree is known to be a kind of decision support tool that is often employed in a tree-like model of decisions and the consequences that follows these decision.

They include the likelihood of event outcomes, resource costs, and others. The reason why the individual is getting bad performance on both the training and test sets is that the decision trees are too shallow and as such one should widen the decision tree to get better result.

Learn more about decision tree from

https://brainly.com/question/26675617