Machine Learning Decision Trees Disadvantages

The model generated can be viewed and hence the approach is a white box. However decision trees also have some disadvantages that we need to be aware of.

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The features on the top are more informative.

Machine learning decision trees disadvantages. For the less informative features we can potentially remove them on subsequent runs. Disadvantages of Decision Tree algorithm The mathematical calculation of decision tree mostly require more memory. Disadvantages of Decision Tree Not good in performance when compared to other Supervised Machine Learning Algorithm.

Due to the presence of highly unbalanced classes it may not work well. Rules generated are understandable. Even though non-linear relationships between various features are not able to influence the performance and efficiency of trees.

Due to overfitting we do Tuning. Decision tree generation and querying is not much computationally expensive. Decision trees can be unstable Try to use the decision tree in ensemble learning Cannot guarantee to return the globally optimal decision tree Training multiple trees in an ensemble learner and take the average of all the decision tree result.

Pros and cons of decision trees. Decision trees are often considered as a non-parametric method they have no opinions about space arrangement and designing of classifiers. The mathematical calculation of decision tree mostly require more time.

This model can handle categorical as well as continuous data. Lets discuss its advantages and disadvantages. The hierarchy of a decision tree model reflects the importance of features.

Lets learn about some disadvantages in decision trees. The main cons are. For a Decision tree.

A small change in the data can cause a large change in the structure of the decision tree causing instability. But the main drawback of Decision Tree is that it generally leads to overfitting of the data. Advantages and Disadvantages of Decision Trees in Machine Learning Decision Tree is used to solve both classification and regression problems.

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

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