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Decision Trees- Definition & the Types of Decision Trees
Data Science is an umbrella term that covers a number of process, tools, techniques and algorithms. Decision Trees are one of the most prominent techniques in the Classification process in Data Science that would be playing a critical role in the decision making process. The process of classification is a two step process that includes learning step & prediction step.
To work on the prediction process in the classification technique, one needs to have expertise in handling the prominent Machine Learning algorithms. In the learning step, the process involves model development and training the model with a known data set. You can build expertise in the prediction and learning process in Data Science with our intense Data Science Training in Hyderabad program.
What are Decision Trees?
Decision Trees are the complex algorithms in the field of Data Science by that belong to the category of the category of Supervised Learning Algorithms. If you are dealing with problems that are related to regression & classification techniques then, working on Decision Trees would be an apt choice.
Decision Trees can help us in developing data models that can predict the class or value of the target variable that can learn from the training data set.
Types of Decision Trees-
Decision Trees can be classified into two different types namely Categorical Variable Decision Trees and Continuous Variable Decision Tress.
- Categorical Variable Decision Tree
In this category, the Decision Tree makes use of a target variable that is categorical in nature.
- Continuous Variable Decision Tree
In this category, if the Decision Tree makes use of a target variable that is continuous in nature.
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