Examples of using A decision tree in English and their translations into Vietnamese
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Computer
Called a decision tree.
They draw a very similar picture, but call it a decision tree.
Building a decision tree.
A decision tree is as simple way to represent a protocol.
Training a decision tree.
The extensive formcan be viewed as a multi-player generalization of a decision tree.
It is called a decision tree.
To create a decision tree, the Random Forest algorithm always starts with an empty decision tree. .
For example a decision tree.
A decision tree can be learned by splitting the source set into subsets based on an attribute value test[1].
Fig.4 Construction of a decision tree.
You can use a decision tree to find out exactly how much you really wanted to see your parents. What do I mean by that? Well, let's see.
A decision tree is the minimum number of yes/no questions that one has to ask, to assess the probability of making a correct decision. .
Fig.4 Construction of a decision tree.
A decision tree describes a tree structure wherein leaves represent classifications and branches represent conjunctions of features that lead to those classifications[1].
Random forest The algorithm is built upon a decision tree to improve the accuracy drastically.
The point where the two lines come together is thepoint of non-linearity, and is the point where a node in a decision tree model would split.
Some people find it helpful to make a decision tree, which lays out every possibility in visual format.
Since actions can be determined from the structure of the game, the tree is properly seen as a game tree, not a decision tree.
Two-staged machine learning: the first to create a decision tree to get the importance of features and the other to train on the most important features.
In the visual novel examples of the genre, the player proceeds in the story by selecting dialogue oraction choices which affect their relationships in a decision tree format.
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including potential event outcomes, resource costs, and utility.
One of the most popular classification algorithms is a decision tree, whereby repeated questions leading to precise classifications can build an“if-then” framework for narrowing down the pool of possibilities over time.
A decision tree essentially starts with a question that has two or more outcomes in turn connecting to other questions, eventually leading to an action, say send an alert or trigger an alarm if analysed data leads to particular answers.
Beginning with the base case, a Decision Tree is an intuitive model whereby one traverses down the branches of the tree and selects the next branch to go down based on a decision at a node.