Decision Trees: A supervised learning algorithm for classification.
Hierarchical tree structure with internal nodes, decision rules, and leaf nodes.
Easy to interpret and understand.
Components of a Decision Tree
Root Node: Topmost node, partitions based on attribute values.
Decision Nodes: Intermediate nodes that represent tests on attributes, leading to branches.
Leaf Nodes: Terminal nodes that represent classification outcomes.
Benefits of Decision Trees
Facilitates understanding of decision-making process.
Allows visualization of decision rules and pathways.
Useful for understanding the 'why' behind decision-making.
Implementation in Code
Libraries Used: NumPy, Pandas, sklearn.
Key Libraries:DecisionTreeClassifier from sklearn.tree, train_test_split from sklearn.model_selection.
Parameters in Classifier: Splitting strategy, maximum depth, minimum samples for split and leaf.
Methods:fit, predict.
Example: Predicting Sleep Patterns
Decision tree example for predicting whether I'll sleep or work based on conditions like the weather and news.
Splitting rules based on attributes such as 'Is it raining?' and 'Do I need sleep?'.
Code Walkthrough
Data Preparation: Import data, manage shape, and ensure balance.
Splitting Data: Divide data into training and testing sets.
Creating Models: Use 'gini' and 'entropy' as criteria for different models.
Prediction and Accuracy: Fit model, make predictions, and calculate accuracy.
Advanced Concepts
Ability to create multiple models and compare results (e.g., 'gini' vs 'entropy').
Using functions for modular and reusable code, especially in team settings.
Printing and interpreting confusion matrices to understand prediction accuracy.
Comparison with Other Algorithms
Logistic Regression: Good for binomial outcomes (e.g., spam detection, tumor classification).
K Nearest Neighbors: Non-parametric, used in pattern recognition and intrusion detection, sensitive to noisy data.
Support Vector Machines (SVM): Suitable for high-dimensional data, complex categories like handwriting recognition.
Strengths and Limitations
Logistic Regression: Easy to implement but requires good data representation. Predicts categorical outcomes.
K Nearest Neighbors: No training period, adaptable, but not good with high-dimensional data and sensitive to outliers.
SVM: Good for high-dimensional data but not suitable for large data sets and noisy data.
Decision Trees: Handles missing values, easy to understand, less training time. Prone to overfitting and less suitable for large data sets.
Practical Application
Loan Repayment Prediction: Predict if a customer will repay a loan using a decision tree. Accuracy of about 94%.
Steps: Data importing and exploration, data splitting, model training, prediction, and accuracy calculation.
Summary
Decision trees are valuable for classification and regression problems. They are intuitive and easy to interpret but require careful management to avoid overfitting. By using Python libraries like sklearn, decision trees can be effectively implemented and utilized for a variety of tasks, from predicting loan repayments to identifying sleeping patterns.