Tree-based Models

Tree-based models power countless production ML systems and remain essential building blocks in more complex models. This guide bridges theory and implementation through coding challenges that mirror actual interview questions. Master practical tree implementations from basic decision trees to optimized ensemble methods, while learning to articulate design choices - exactly what interviewers expect for ML engineer and researcher roles.

Core Knowledge

Fundamental Concepts

• Split criterion implementations:

• Split types:

Tree Construction

• Recursive partitioning algorithms:

• Growth heuristics:

Data Handling

• Feature processing:

• Missing value strategies:

Optimization Techniques

• Stopping criteria:

• Pruning implementations:

• Regularization:

Implementation Considerations

• Tree representation:

• Performance optimizations:

• Space-time tradeoffs:

Advanced Functionality

• Interpretability features:

Scalability Patterns

• Large dataset handling:

• Approximate methods:

• Streaming adaptations:

Key Questions

Status	Question	Category
	Implement a Binary Decision Tree Classifier for Categorical Features	Tree-based Models
	Optimal Split Point Finding for Continuous Feature Handling	Tree-based Models
	Regression Tree Implementation with MSE-based Splitting	Tree-based Models
	Implement Decision Tree Classifier with Multiple Pre-Pruning Techniques	Tree-based Models
	Implement Reduced Error Post-Pruning for Decision Trees	Tree-based Models
	Calculate Feature Importance Using Gini Importance Accumulation	Tree-based Models
	Decision Path Extraction and Feature Contribution Tracking for Model Interpretation	Tree-based Models

Common Pitfalls

Extended Questions

Status	Question	Category
	Memory-Efficient Tree Storage with Compact Node Representation and Null Split Compression	Scalability Challenges
	Quantile-Based Approximate Split Finding for Regression Trees	Scalability Challenges
	Parallel Feature Evaluation for Split Finding	Scalability Challenges
	Implement CSR Sparse Matrix Support for Split Calculations in CART Trees	Advanced Data Support
	Optimize Split Finding with Vectorized Impurity Calculations	Optimization & Efficiency
	Histogram-Based Split Finding for Decision Trees with Incremental Updates	Optimization & Efficiency
	Memory-Efficient Tree Storage with Node Poolin	Optimization & Efficiency

Tree-based Models

Core Knowledge

Fundamental Concepts

Tree Construction

Data Handling

Optimization Techniques

Implementation Considerations

Advanced Functionality

Scalability Patterns

Key Questions

Common Pitfalls

Incorrect impurity calculation leading to suboptimal splits

Not handling edge cases (e.g., all samples belong to same class)

Inefficient recursive implementation causing stack overflows

Poor handling of continuous features (failing to sort features)

Memory inefficiency in node storage for large datasets

Extended Questions

Core Knowledge

Fundamental Concepts

◦ Classification: Gini impurity (CART)

◦ Classification: Information gain (ID3)

◦ Regression: Variance reduction

◦ Regression: Mean squared error (MSE)

◦ Binary vs. multi-way splits

◦ Threshold comparison strategies

Tree Construction

◦ ID3 (categorical features)

◦ CART (continuous/categorical features)

◦ Depth-first vs. best-first expansion

◦ Leaf node purity thresholds

Data Handling

◦ Continuous: Efficient split finding via sorting (O(n log n))

◦ Categorical: Encoding tradeoffs (one-hot vs. ordinal)

◦ Surrogate splits

◦ Majority direction fallback

Optimization Techniques

◦ Max depth constraints

◦ Minimum samples per leaf

◦ Impurity improvement thresholds

◦ Pre-pruning (early stopping)

◦ Post-pruning (cost complexity pruning)

◦ Depth penalties

Implementation Considerations

◦ Class-based node structure

◦ Memory-efficient serialization

◦ Vectorized impurity calculations

◦ Parallel feature evaluation

◦ Sparse matrix support

◦ Node pooling techniques

Advanced Functionality

◦ Gini/permutation importance

◦ Decision path tracing

Scalability Patterns

◦ External sort for feature values

◦ Disk-based node storage

◦ Quantile sketch split points

◦ Histogram based split finding

◦ Randomized threshold sampling

◦ Online histogram updates

◦ Dynamic stop conditions

Key Questions

Common Pitfalls

Incorrect impurity calculation leading to suboptimal splits

Not handling edge cases (e.g., all samples belong to same class)

Inefficient recursive implementation causing stack overflows

Poor handling of continuous features (failing to sort features)

Memory inefficiency in node storage for large datasets

Extended Questions