Decision Tree

Pros & Cons

decision tree


1- Easy Interpretation

This is where Decision Trees get praised most. Since it's constructed of one single tree in such a straightforward way, you can directly observe Decision Tree in the working or easily interpret its results.

2- No Normalization

Doesn't require normalization

3- Easy Data Preperation

On top of no normalization requirement, decision trees also don't require scaling or pre-handling missing data.

These factors make decision tree a champ when it comes to easy data preparation and it provides overall great quick and dirty results.

4- Missing Data

Handles missing values, useful for feature engineering.

5- Fast Training (may be wrong)

Training process is relatively faster for decision trees compared to some other algorithms such as random forests.

This makes sense since random forest deals with multiple trees and decision tree is concerned with a single decision tree.

6- Optimization Options

Although Decision Trees are simple to interpret, it doesn't mean they are always simple to implement.

There are lots parameters that can affect the results of a decision tree algorithm which gives you more control over the results, efficiency and performance.

However, if you want to optimize decision trees, there can be a learning curve and expect a new terminology of; nodes, leaves, tree size, pruning etc.

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decision tree


1- Overfitting Risk

This risk is considerably high with decision trees and they do tend to get stuck in local minimas. This can destroy the machine learning experience.

2- No Regression

Although there are other varieties (think regression trees) If you need to predict continuous values through regression you are out of luck with decision trees as they are strictly classification models and can't be used for regression.

3- Inadequate prediction powers

Decision trees, although a great idea fundamentally, can be limited when it comes to prediction powers. When there are lots of features and a complex large datasets, random forests start to show off their prediction potential.

4- Imbalance bias

If dataset is not balanced this can cause a bias. Balancing dataset in advance fixes this problem.


Decision Tree Pros & Cons Summary

Why Decision Trees?

Sometimes you or your audience just needs good straightforward communication. That's where Decision Trees shine. Outputs will be plain vanilla and easy to interpret. If you're not super concerned about the prediction accuracy Decision Trees might be the solution for you.

Easy Interpretation

This gotta be the biggest edge of Decision Trees. They just give easy, readable outputs.

Easy Data Prep

Data prep is easy with Decision Trees on so many levels. (missing data is ok,no normalization, no scaling etc.)

Computation Cost

Compared to some algorithms such as random forests, decision trees are a lighter alternative.

Parameter Complexity

Decision Trees come with a learning curve especially if you want to get hands-on with them. There are lots of important parameters that can make a big difference.

Relatively Slow

Despite its edge on Random Forests, Decision Trees are computationally expensive in general.

Limited Power

Decision tree can be limited in its accuracy and tackling complex data.