Decision Tree
Pros & Cons
decision tree
Advantages
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
Disadvantages
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.
wrap-up
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.
