Summary of "Decision and Classification Trees, Clearly Explained!!!"
Main Ideas
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Definition of Decision Trees:
- A decision tree is a flowchart-like structure that makes decisions based on true or false statements.
- Classification Trees categorize data, while regression trees predict numeric values.
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Structure of Decision Trees:
- Root Node: The top node of the tree.
- Internal Nodes: Branches that represent decisions based on data.
- Leaf Nodes: Endpoints that represent classifications or outcomes.
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Building a Classification Tree:
- Start with raw data and determine the best feature to split the data at the root.
- Use measures like impurity (e.g., Gini Impurity) to evaluate the effectiveness of splits.
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Calculating Gini Impurity:
- Gini Impurity measures the impurity of leaves and helps in choosing the best feature for splits.
- The formula involves calculating the probabilities of different outcomes (e.g., yes or no) and their squares.
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Selecting Features:
- Compare Gini Impurity values for different features to decide which should be at the top of the tree.
- The feature with the lowest impurity is chosen for the split.
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Handling Numeric Data:
- For numeric features, thresholds are established to create splits, and Gini Impurity is calculated for each threshold.
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Overfitting:
- Overfitting occurs when a model is too complex and captures noise in the data.
- Solutions include pruning the tree or limiting the number of samples per leaf.
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Cross Validation:
- A method to test different configurations (like the minimum number of samples per leaf) to find the best-performing model.
Methodology for Building a Classification Tree
- Start with raw data.
- Determine the best feature to split the data using Gini Impurity.
- Calculate Gini Impurity for each feature:
- For each leaf, calculate the impurity using the formula:
Gini Impurity = 1 - ∑ (p_i^2) - Where
p_iis the probability of each class in the leaf.
- For each leaf, calculate the impurity using the formula:
- Choose the feature with the lowest Gini Impurity for the root.
- Repeat the process for subsequent nodes until leaves are pure or meet a stopping criterion.
- Assign output values to leaves based on majority class.
- Evaluate the tree for Overfitting and adjust as necessary.
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