advantages and disadvantages of decision tree in Real-World Applications

Depending on the type of problem being solved, decision trees have different advantages and disadvantages of decision tree. A decision tree is a visual representation of various solutions to a problem under different suppositions. Similar to other tree-based data structures including the balanced selection tree (BST), binary tree (BT), and alternating complete (AVL) tree, a decision tree has the same structure. A decision tree can be created manually, with the aid of software such as a graphical editor, or using other tools. In plain English, decision trees assist in focusing conversation during group deliberation.

advantages and disadvantages of decision tree

The following lists the advantages and disadvantages of decision tree

Advantages:

It works for both classification and regression problems: Decision trees can be used to predict both continuous and discrete values since they work well in both classification and regression issues.

Decision trees lessen the cognitive load involved in learning an algorithm since they are simple.

They can be used to classify data that doesn't have a clear hierarchy.

When working with non-linear data, the decision tree approach does not require changing the features because it does not take into account numerous weighted combinations at once.

They are substantially quicker and more effective than KNN and other classification methods.

Any type of data, including boolean values, categories, and numbers, can be processed by decision trees.

Normalization is not required while utilising the Decision Tree.

In contrast to several other machine learning techniques, the decision tree does not require us to think about feature scaling. Random forests are another option. The performance of the in question algorithms is not affected by the size of the input.

This knowledge can help us comprehend the worth of particular qualities.

a resourceful way to sort through data Using a decision tree is one of the quickest ways to identify the key elements of a scenario and the relationships among them. Using decision trees improves the potential to produce more variables/features for the output variable.

The need to tidy up data will diminish: The decision tree can work with less information because the presence of an outsider or missing data at a node of the tree does not affect the outcome.

Decision trees don't rely on any particular set of measurements, in contrast to conventional statistical techniques. A non-parametric technique is used to avoid making arbitrary assumptions about the geographical distribution and classifier structure.

Disadvantages:

Here is how the decision tree was divided for millions of records with numerical variables: Since the complexity of a decision tree increases exponentially with the amount of records, training one with just two numerical variables takes a lot of time.

This is also true for techniques like XGBoost and random forests.

The decision tree with several features Be patient; the complexity of the training process increases as the input does.

Learning from the tree of the training set: Overfit trimming and random forest ensemble learning are used for pre- and post-processing.

Overfitting as a strategy: Overfitting is one of the trickiest approaches to learn when it comes to decision tree models. The overfitting problem can be resolved by imposing restrictions on the model's parameters and the pruning process.

You are aware that while creating a decision tree, overfitting data is frequently necessary. Significant output variance brought on by the overfitting problem leads to multiple estimation errors and, perhaps, findings that are quite wrong. Overfitting, which completely eliminates bias, raises variability.

Even if a decision tree's data can be reused, it's possible that minor adjustments will radically alter the tree's outcome. This decision tree variation can be decreased by using strategies like boosting and bagging.

The use of it with large datasets is useless: A single tree is more likely to have many nodes when the dataset is larger, which might increase complexity and result in overfitting.

It's possible that the ideal decision tree, which would be 100% effective, won't be provided.