Decision Tree Implementation
1. Problem Statement:
Your company specializes in cultivating and selling wild mushrooms, catering to both culinary enthusiasts and health-conscious consumers. However, ensuring the safety of your products is paramount, as consuming poisonous mushrooms can lead to severe health issues or even fatalities. Therefore, you need a robust classification system to distinguish between edible and poisonous mushrooms based on their physical characteristics.
2. Dataset:
Your dataset comprises a diverse collection of mushrooms, each described by a set of features that are crucial for identification. These features include not only visual attributes like cap color and stalk shape but also other characteristics such as odor, habitat, and spore print color. However, for the sake of simplicity in this example, we’ll focus on just a few key features.
3. One-Hot Encoded Dataset:
To prepare the dataset for training a machine learning model, you’ve encoded categorical features like cap color and stalk shape using one-hot encoding. This technique transforms categorical variables into a binary format, where each category becomes a separate binary feature. For example, “Brown Cap” might be represented as [1, 0] while “Red Cap” is [0, 1].
4. Decision Tree Refresher:
Decision trees are a popular supervised learning method used for classification and regression tasks. They work by recursively partitioning the feature space into regions, with each partition corresponding to a decision rule based on a feature’s value. At each node of the tree, the algorithm selects the feature that best splits the data, aiming to maximize the homogeneity (or purity) of the resulting subsets.
5. Implementation Steps:
a. Calculate Entropy:
Entropy quantifies the uncertainty or impurity of a dataset’s label distribution. In the context of decision trees, we calculate entropy to measure the diversity of class labels at a particular node.
b. Split Dataset:
Based on a chosen feature and its possible values, we split the dataset into subsets, with each subset containing examples that exhibit a specific value for the chosen feature.
c. Calculate Information Gain:
Information gain measures the effectiveness of a feature in reducing uncertainty about the class labels. It is calculated as the difference between the entropy of the parent node and the weighted sum of the entropies of its child nodes.
d. Get Best Split:
By computing the information gain for each feature, we can determine which feature provides the greatest reduction in entropy, indicating the most informative split.
6. Building the Tree:
The decision tree construction process involves recursively selecting the best feature to split on, partitioning the dataset, and growing the tree until a stopping criterion is met. Common stopping criteria include reaching a maximum tree depth, achieving perfect purity, or when further splits fail to yield significant information gain.
7. Handling Overfitting:
Decision trees have a tendency to overfit the training data, capturing noise and irrelevant patterns that don’t generalize well to unseen examples. To mitigate overfitting, techniques such as pruning (removing unnecessary branches) or using ensemble methods like random forests can be employed.
Conclusion:
Implementing a decision tree classifier from scratch provides a deep understanding of how these models make decisions based on the data’s features. While decision trees offer interpretability and simplicity, they may not always be the best choice for complex datasets with high-dimensional feature spaces. Nonetheless, they serve as an excellent starting point for learning about machine learning algorithms and classification techniques.
Attached is the GitHub link for Decision Tree Implementation:
https://github.com/aanchalparegi/Decision-Tree-Implementation
Feel free to check it out!
