A decision tree is a tree-like structure support tool that models possible outcomes, cost of resources, services, and possible significance. Decision trees provide a way to handle algorithms with conditional control statements. This includes branches that constitute decision-making steps that can lead to a commending result.
A decision tree is a very popular and well-designed tool for categorization and forecast. A Decision tree is a flow diagram-like tree structure, where each internal node denotes a test on an aspect, each branch represents an outcome of the test, and each terminal node holds a class label.
If we have to figure out collective outcomes that must be made in sequence then we need decision trees for such tasks and when there is an unpredictability about events. For such reasons, decision trees are very productive and help to evaluate capacity expansion alternatives given that future demand is unpredictable.
The main decision is whether to purchase a large facility or a small one with the possibility of expansion later. You can see that the decision to expand later is dependent on choosing a small facility now. Which alternative ends up being best will depend on whether demand turns out to be high or low? Unfortunately, we can only forecast future demand and have to incur some risks.
Types of Decisions:-
Based on the target variable, there are two main types of decision trees:-
- Continuous variable decision trees
- Categorical variable decision trees
- Continuous variable decision tree
A continuous variable decision tree is a decision tree with a continuous variable target. For instance, based on information such as age, profession, and other continuous variables we can predict the income of an individual which was not known to us.
- Categorical variable decision tree
Categorical target variables that are divided into categories are known as a categorical variable decision tree. Such as, the categories can be a simple yes or no. The categories mean that every stage of the decision process falls into one of the categories.
Decision Tree approach – Strengths vs. Weakness
The strengths of decision tree methods are:
- Decision tree software can generate understandable rules –
Due to its simplicity, anyone can code, visualize, interpret, and manipulate simple decision trees, such as the naive binary type. Even for beginners, the decision tree classifier is easy to learn and understand. It requires its user’s minimal effort for data preparation and analysis.
- It can work with excellent accuracy –
The decision tree follows a non-parametric method, which means that it is distribution-free and does not depend on probability distribution assumptions. Also, it can function with excellent accuracy on high-dimensional data.
- Limited data cleaning required –
One of the many advantages of decision trees is that there is limited data cleaning required once the variables have been created. Chances of missing outliers and values have less significance on the decision tree’s data.
- Training time of decision tree is faster in comparison to the latter –
Being a white box type of ML algorithm, a decision tree works with an internal decision-making logic, this means that the acquired knowledge from a data set can be conveniently obtained in a readable form. Such as Neural Network, which is not a feature of black-box algorithms. This makes the training time of the decision tree faster in comparison to the latter.
- Easy to prepare –
Compared to other decision techniques, decision trees take less effort for data preparation. Users, however, need to have ready information 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.
Decision trees can work on both categorical and numerical data –
Decision trees can effectively perform variable screening or feature selection. They can easily work on both categorical and numerical data. Furthermore, they can handle problems with multiple outputs or results.
There are quite many features of a decision tree like it can provide us with a clear indication of which fields are most important for prediction or classification and decision trees can handle both continuous and categorical variables. It can even perform classification without requiring much computation.
The weaknesses of decision tree methods:
- Unreliable nature compared to other decision predictors –
Decision trees have a limitation in that they are largely unstable or un-predictable when compared with other decision predictors. A small change in the data can lead to a major difference 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 bagging and boosting.
- Less effective in predicting the outcome of a continuous variable –
Decision trees are less effective in making predictions when the main goal is to predict the outcome of a continuous variable. This happens because decision trees tend to lose information when classifying variables into numerous categories.
Applications of Decision Trees
- 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.
- 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.
- Serving as a support tool in several fields –
Lenders also use decision trees to foresee the possibility 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 credit-worthiness of a customer to prevent any business loss.
Decision trees are very useful in the field of operations, research in planning logistics, and strategic management. They can help in determining the best strategies that will guide a company to achieve its business goals. Other fields where decision trees can be applied include law, engineering, education, business, healthcare, and finance.
One of the advantages of decision tree software platform is that their outputs are easy to read and interpret, without even 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.