Decision tree learning

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Decision Tree

Decision tree learning is a popular method used in machine learning, data mining, and statistics for predictive modelling. It involves the creation of decision trees, which are simple, yet powerful tools for classification and regression tasks. Decision trees help in making decisions based on pre-defined rules, making them easy to understand and interpret.

Overview

A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g., whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules.

In essence, decision tree learning is about learning a sequence of if-else questions that lead us to the correct decision. It falls under the category of supervised learning, where the goal is to learn a model from labeled training data that can be used to make predictions about unseen or future data.

Types of Decision Trees

There are mainly two types of decision trees:

1. Classification Trees: These are used when the target variable is categorical. The goal is to predict a class label for each instance in the data. 2. Regression Trees: These are used when the target variable is continuous. The goal is to predict a value for each instance in the data.

Algorithm

The core algorithm for building decision trees called ID3 (Iterative Dichotomiser 3) involves the following steps: 1. Start with all the training examples at the root. 2. Select the attribute that best differentiates the examples according to a heuristic. The most common heuristic is the information gain. 3. Divide the examples into subsets using the selected attribute, creating a node for each subset. 4. Recursively apply the above steps to each subset until one of the following conditions is met:

  - All elements in the subset belong to the same class.
  - There are no more attributes to be selected, but the examples still do not belong to the same class (handled by majority voting).
  - There are no examples in the subset, which can happen if no example in the parent set was found to match a specific value of the selected attribute.

Advantages and Disadvantages

Advantages

- Simple to understand and interpret. Trees can be visualised. - Requires little data preparation. Other techniques often require data normalization, dummy variables need to be created and blank values to be removed. - The cost of using the tree (i.e., making predictions) is logarithmic in the number of data points used to train the tree.

Disadvantages

- Decision trees can create over-complex trees that do not generalize well from the training data (overfitting). - Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. - Decision tree learners create biased trees if some classes dominate. It is therefore recommended to balance the dataset prior to fitting with the decision tree.

Applications

Decision tree learning is widely used in various domains such as: - Credit scoring - Medical diagnosis - Stock market analysis - Agriculture - Energy consumption forecasting

See Also

- Machine Learning - Random Forests - Boosting (Machine Learning) - Supervised Learning

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