In decision trees
In decision trees
The correct option is A Nodes represent the condition, with the right side of tree hightailing the actions to be taken
In decision trees Nodes represent the condition,
with the right side of tree hightailing the actions to be taken
A decision tree is a decision support tool that
uses a tree-like graph or model of decisions and their possible consequences,
including chance event outcomes, resource costs, and utility. It is one way to
display an algorithm that only contains conditional control statements.
A decision tree is built top-down from a root node
and involves partitioning the data into subsets that contain instances with
similar values (homogenous). ID3 algorithm uses entropy to calculate the
homogeneity of a sample.
These graphs have nodes representing decision
points about the main variable given the predictor and edges are the
connections between the nodes. ... As the goal of a decision tree is that it
makes the optimal choice at the end of each node it needs an algorithm that is
capable of doing just that.
Gini impurity is a measure of misclassification,
which applies in a multiclass classifier context. Gini coefficient applies to
binary classification and requires a classifier that can in some way rank
examples according to the likelihood of being in a positive class.
In decision trees Nodes represent the condition, with the right side of tree hightailing the actions to be taken
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogenous). ID3 algorithm uses entropy to calculate the homogeneity of a sample.
These graphs have nodes representing decision points about the main variable given the predictor and edges are the connections between the nodes. ... As the goal of a decision tree is that it makes the optimal choice at the end of each node it needs an algorithm that is capable of doing just that.
Gini impurity is a measure of misclassification, which applies in a multiclass classifier context. Gini coefficient applies to binary classification and requires a classifier that can in some way rank examples according to the likelihood of being in a positive class.
