Top 6 Advantages and Disadvantages of Decision Tree Algorithm

Decision Tree is one the most useful machine learning algorithm. Decision tree can be used to solve both classification and regression problem.

When we use data points to create a decision tree, every internal node of the tree represents an attribute and every leaf node represents a class label.

Like any other machine learning algorithm, Decision Tree algorithm has both disadvantages and advantages.

You may like to watch a video on Decision Tree from Scratch in Python

Advantages of Decision Tree algorithm

When using Decision tree algorithm it is not necessary to normalize the data.

Decision tree algorithm implementation can be done without scaling the data as well.

When using Decision tree algorithm it is not necessary to impute the missing values.

The data pre-processing step for decision trees requires less code and analysis.

The data pre-processing step for decision trees requires less time.

The concept behind decision tree is more familer to programmers and comparatively easier to understand than other similar algorithms.

Disadvantages of Decision Tree algorithm

The mathematical calculation of decision tree mostly require more memory.

The mathematical calculation of decision tree mostly require more time.

The reproducibility of decision tree model is highly sensitive as small change in the data can result in large change in the tree structure.

The space and time complexity of decision tree model is relatively higher.

Decision tree model training time is relatively more as complexity is high.

Single Decision tree is often a weak learner so we require a bunch of decision tree for called random forest for better prediction.

Hence before implementing Decision Tree we need to brainstorm , whether it is suitable for our problem statement or not.

Happy Coding !!

If you like to check out Logistic Regression in Python from Scratch please watch video:

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