It has a tough exterior. Despite its limitations and drawbacks, decision trees are still effective at splitting data and creating predictive models.You can learn more about ML by joining Machine Learning Coaching In Bangalore by Tutort Academy.

1. DECISION TREE IN MACHINE
LEARNING

2. DECISION TREE IN MACHINE LEARNING
• Predictive models are at the heart of many aspects of the data science
world. A good model is directly dependent on the algorithm that a data
scientist chooses. However, because there are so many algorithms, data
scientists frequently struggle to choose the best one. The decision tree
algorithm is critical in making an informed decision. This article will discuss
the benefits and drawbacks of the decision tree algorithm in machine
learning.

3. ADVANTAGES
• ● Interpretability
• One of the most significant Decision tree benefits is that it is highly intuitive
and simple to grasp. Furthermore, the rules implemented by decision trees
can be displayed in a flow chart-like format, allowing data scientists and
other professionals to explain the model’s predictions to stakeholders.

4. ADVANTAGES
● Reduced Data Preparation
• Data preparation is a significant challenge when developing a model that
involves other algorithms. This is due to the fact that any model operates on
the ‘garbage in, garbage out principle, which states that the quality of
predictions made by the model is dependent on the quality of data fed to
the model to train on, and this is where decision trees excel.

5. ADVANTAGES
● Non-Parametric
Algorithms such as linear regression, naive Bayes, and others require a
number of assumptions to be met in order for the model to function properly.
As previously stated, Decision Trees is a non-parametric algorithm, so no
significant assumptions or data distribution must be considered.

6. ADVANTAGES
● Non-Linearity
Decision Trees can define complex decision boundaries, allowing them to
solve non-linear problems with ease.

7. DISADVANTAGES
● Overfitting
• One of the most common and obvious drawbacks of decision trees is that it
is a high-variance algorithm. This means that it can easily overfit because it
lacks an inherent stopping mechanism, resulting in complex decision rules.
● Data Resampling and Feature Reduction
• A decision tree’s training phase can be extremely time-consuming, and this
problem can be exacerbated if there are multiple continuous independent
variables.

8. DISADVANTAGES
● Optimization
The decision tree algorithm looks for the pure node at each level and does not
consider how the most recent decision will affect the next few stages of
splitting. This is why it is referred to as a greedy algorithm.

9. ENDNOTES
The decision tree algorithm is one of the most widely used predictive
modeling algorithms. It has a tough exterior. Despite its limitations and
drawbacks, decision trees are still effective at splitting data and creating
predictive models.
• You can learn more about ML by joining Machine Learning
Coaching In Bangalore by Tutort Academy.
•

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