Decision Tree
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| Decision Treee |
What is a Decision Tree?
A decision tree
is a support tool with a tree-like structure that models probable outcomes,
cost of resources, utilities, and possible consequences.
Decision trees
provide a way to present algorithms with conditional control statements. They
include branches that represent decision-making steps that can lead to a
favorable result.
Decision
Trees
The flowchart structure includes internal nodes that represent tests or attributes at each
stage. Every branch stands for an outcome for the attributes, while the path
from the leaf to the root represents rules for classification.
Decision trees are one of the best forms of learning algorithms
based on various learning methods. They boost predictive models with accuracy,
ease in interpretation, and stability.
Decision trees are used for handling non-linear data sets
effectively.
The decision tree tool is used in real life in many areas, such as
engineering, civil planning, law, and business.
Decision trees can be divided into two types; categorical variable
and continuous variable decision trees.
Types of Decisions
There are two main types of decision trees that are based on the
target variable, i.e., categorical variable decision trees and continuous
variable decision trees.
1. Categorical variable decision tree
A categorical variable decision tree includes categorical target
variables that are divided into categories. For example, the categories can be
yes or no. The categories mean that every stage of the decision process falls
into one of the categories, and there are no in-betweens.
2. Continuous variable decision tree
A continuous variable decision tree is a decision tree with a
continuous target variable. For example, the income of an individual whose
income is unknown can be predicted based on available information such as their
occupation, age, and other continuous variables.
Applications of Decision Trees
1. Assessing prospective growth opportunities
One of the applications of decision trees involves evaluating
prospective growth opportunities for businesses based on historical data.
Historical data on sales can be used in decision trees that may lead to making
radical changes in the strategy of a business to help aid expansion and growth.
2. Using demographic data to find prospective clients
Another application of decision trees is in the use of demographic
data to find prospective clients. They can help in streamlining a marketing
budget and in making informed decisions on the target market that the business
is focused on. In the absence of decision trees, the business may spend its
marketing market without a specific demographic in mind, which will affect its
overall revenues.
3. Serving as a support tool in several fields
Lenders also use decision trees to predict the probability of a
customer defaulting on a loan, by applying predictive model generation using
the client’s past data. The use of a decision tree support tool can help
lenders in evaluating the creditworthiness of a customer to prevent losses.
Decision trees can also be used in operations research in planning
logistics and strategic management. They can help in determining appropriate
strategies that will help a company achieve its intended goals. Other fields
where decision trees can be applied include engineering, education, law,
business, healthcare, and finance.
Advantages of Decision Trees
1. Easy to read and
interpret
One of the advantages of decision trees is that their outputs are
easy to read and interpret, without even requiring statistical knowledge. For
example, when using decision trees to present demographic information on
customers, the marketing department staff can read and interpret the graphical
representation of the data without requiring statistical knowledge.
The data can also be used to generate important insights on the
probabilities, costs, and alternatives to various strategies formulated by the
marketing department.
2. Easy to prepare
Compared to other decision techniques, decision trees take less
effort for data preparation. Users, however, need to have ready information in
order to create new variables with the power to predict the target variable.
They can also create classifications of data without having to compute complex
calculations. For complex situations, users can combine decision trees with
other methods.
3. Less data cleaning required
Another advantage of decision trees is that, once the variables
have been created, there is less data cleaning required. Cases of missing
values and outliers have less significance on the decision tree’s data.
Disadvantages of Decision Trees
1. Unstable nature
One of the limitations of decision trees is that they are largely
unstable compared to other decision predictors. A small change in the data can
result in a major change in the structure of the decision tree, which can
convey a different result from what users will get in a normal event. The
resulting change in the outcome can be managed by machine learning algorithms,
such as boosting and bagging.
2. Less effective in
predicting the outcome of a continuous variable
In addition, decision trees are less effective in making
predictions when the main goal is to predict the outcome of a continuous
variable. This is because decision trees tend to lose information when
categorizing variables into multiple categories.
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