Why decision tree is fast?
Very Fast Decision Tree (VFDT) in data stream mining has been widely studied for more than a decade. VFDT in essence can mine over a portion of an unbounded data stream at a time, and the structure of the decision tree gets updated whenever new data feed in; hence it can predict better upon the input of fresh data.
Which algorithm can be used for building decision tree?
The ID3 algorithm builds decision trees using a top-down greedy search approach through the space of possible branches with no backtracking. A greedy algorithm, as the name suggests, always makes the choice that seems to be the best at that moment.
Is decision tree computationally expensive?
Decision tree can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting field must be sorted before its best split can be found.
Which is the best algorithm for a decision tree?
Let us look at some algorithms used in Decision Trees: The ID3 algorithm builds decision trees using a top-down greedy search approach through the space of possible branches with no backtracking. A greedy algorithm, as the name suggests, always makes the choice that seems to be the best at that moment.
How are decision trees used in machine learning?
Decision tree is one of the most popular machine learning algorithms used all along, This story I wanna talk about it so let’s get started!!! Decision trees are used for both classification and regression problems, this story we talk about classification.
How does a decision tree arrive at an estimate?
A decision tree is arriving at an estimate by asking a series of questions to the data, each question narrowing our possible values until the model get confident enough to make a single prediction. The order of the question as well as their content are being determined by the model.
Which is better a decision tree or a tree based model?
In a nutshell Decision trees and tree based models in general just do a clever nearest neighbours. In this kind of problems where any tree based algorithm is useless neural net or linear regression model are the preferred models.