Introduction to Decision Trees
As a type of supervised machine learning algorithm, Decision Tree Learning has a variety of classification and regression applications. One of the biggest advantages of decision trees is that they are intuitive and easy to understand and interpret, even for non-specialists. Decision trees can even be visualized with a structure that mimics the tree to help describe human decision processes. Decision trees are effective for use in many AI applications, such as in finance, healthcare, and marketing.
What is Decision Tree Learning?
Unlike many machine learning applications, which are represented with clouds of data points, decision tree learning represents a more ‘human’ approach to data analytics. Using a tree structure, it can show the various options or branches that can occur from each decision made, and end with a leaf to signify the final outcome or decision.
What is the structure of Decision Tree Learning?
- – Root Node – represents the whole dataset
- – Decision Node – analyzes or tests a particular feature
- – Branch – represents the outcome or result of a decision
- – Leaf Node – final output which can be referred to as a class label
How Does Decision Tree Learning Work?
The algorithm begins with identifying the most relevant or key feature from the dataset at hand. From there the dataset gets divided or partitioned based on that feature. Then repeat and partition each subset that was created. This process is called recursive partitioning. This pattern of partitioning is repeated until a defined condition is met. Leaf nodes are used to store final outcomes or pieces of predictions at the end of the process.
Measures of Attribute Selection
- Information Gain
- Gini Index
- Gain Ratio
- Chi-Square Test
These criteria help determine the most appropriate attribute for splitting the data.
Decision Tree Types
- Classification Trees – Outputs a class label.
- Regression Trees – Outputs a continuous value.
- Binary Trees – Every node has two branches.
- Multi-way Trees – Nodes can have multiple branches.
Benefits of Decision Tree Learning
- It is straightforward and easy to grasp.
- Lesser data preprocessing is needed.
- It accommodates numerical and categorical data.
- The visual representation helps to interpret.
- The learning and prediction processes are rapid.
Downsides of Decision Tree Learning
- It can fit the data too closely.
- It can be uneven to small alterations in data.
- It is less precise than ensemble techniques.
- It is biased to features that have more levels.
Examples of Decision Tree Learning
- Medical diagnostics
- Credit approval systems
- Capturing fraud
- Predicting customer churn
- Recommender systems
Questions That Are Often Asked and Answered
Q1. Does Decision Tree Learning operate under supervision or no supervision?
Answer: Decision Tree Learning is supervised learning.
Q2. What is the primary purpose of a Decision Tree?
Answer: To divide the data into smaller and more homogeneous subsets in order to make accurate predictions.
Q3. Which metric is frequently utilized to divide the nodes?
Answer: The most frequently used are Information Gain and Gini Index.
Q4. Is it true that decision trees deal with missing values?
Answer: Yes. Decision trees deal with missing values through something called surrogate splits.
Q5. Why are decision trees embraced in machine learning?
Answer: Because of their explainability and structure, they are often compared to human thinking.
