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Sunday, 24 February 2019

Advantages and Disadvantages of Decision Trees in Machine Learning

Decision Tree is used to solve both classification and regression problems. But the main drawback of Decision Tree is that it generally leads to overfitting of the data. Lets discuss its advantages and disadvantages in detail.

Advantages of Decision Tree

1. Clear VisualizationThe algorithm is simple to understand, interpret and visualize as the idea is mostly used in our daily lives. Output of a Decision Tree can be easily interpreted by humans.

2. Simple and easy to understand: Decision Tree looks like simple if-else statements which are very easy to understand.

3. Decision Tree can be used for both classification and regression problems.

4. Decision Tree can handle both continuous and categorical variables.

5. No feature scaling required: No feature scaling (standardization and normalization) required in case of Decision Tree as it uses rule based approach instead of distance calculation.

6. Handles non-linear parameters efficiently: Non linear parameters don't affect the performance of a Decision Tree unlike curve based algorithms. So, if there is high non-linearity between the independent variables, Decision Trees may outperform as compared to other curve based algorithms.

7. Decision Tree can automatically handle missing values.

8. Decision Tree is usually robust to outliers and can handle them automatically.

9. Less Training Period: Training period is less as compared to Random Forest because it generates only one tree unlike forest of trees in the Random Forest. 

Disadvantages of Decision Tree

1. Overfitting: This is the main problem of the Decision Tree. It generally leads to overfitting of the data which ultimately leads to wrong predictions. In order to fit the data (even noisy data), it keeps generating new nodes and ultimately the tree becomes too complex to interpret. In this way, it loses its generalization capabilities. It performs very well on the trained data but starts making a lot of mistakes on the unseen data.

I have written a detailed article on Overfitting here.

2. High variance: As mentioned in point 1, Decision Tree generally leads to the overfitting of data. Due to the overfitting, there are very high chances of high variance in the output which leads to many errors in the final estimation and shows high inaccuracy in the results. In order to achieve zero bias (overfitting), it leads to high variance. 

3. Unstable: Adding a new data point can lead to re-generation of the overall tree and all nodes need to be recalculated and recreated. 

4. Affected by noise: Little bit of noise can make it unstable which leads to wrong predictions.

5. Not suitable for large datasets: If data size is large, then one single tree may grow complex and lead to overfitting. So in this case, we should use Random Forest instead of a single Decision Tree.

In order to overcome the limitations of the Decision Tree, we should use Random Forest which does not rely on a single tree. It creates a forest of trees and takes the decision based on the vote count. Random Forest is based on bagging method which is one of the Ensemble Learning techniques.

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I have more than 10 years of experience in IT industry. Linkedin Profile

I am currently messing up with neural networks in deep learning. I am learning Python, TensorFlow and Keras.

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