Forest Cover Type Prediction

A machine learning solution for the Kaggle Forest Cover Type Prediction competition, achieving competitive scores through advanced feature engineering and ensemble methods.

Competition Overview

The goal is to predict the forest cover type (7 categories) for 30m x 30m cells in Roosevelt National Forest, Colorado, using only cartographic variables—no remotely sensed data.

Cover Types:

1. Spruce/Fir
2. Lodgepole Pine
3. Ponderosa Pine
4. Cottonwood/Willow
5. Aspen
6. Douglas-fir
7. Krummholz

Solution Approach

Feature Engineering

Feature Category	Description
Distance Metrics	Euclidean & Manhattan distances to hydrology
Distance Ratios	Hydrology/Road, Hydrology/Fire, Road/Fire ratios
Elevation Interactions	Combined elevation with vertical hydrology distance
Hillshade Statistics	Mean and range across time points (9am, noon, 3pm)
Aspect Decomposition	North-South and East-West components (cos/sin transform)
Categorical IDs	Wilderness Area ID, Soil Type ID from one-hot columns
Domain Features	Climate zone, stony soil indicator

Target Encoding

Cross-validation target encoding with smoothing to prevent data leakage:

• Encoding computed only on training folds
• Test set receives averaged encodings across all folds
• Smoothing blends category mean with global mean for rare categories

Models

Three gradient boosting models with optimized hyperparameters:

Model	Key Strengths
LightGBM	Fast training, efficient memory usage
XGBoost	Robust regularization, histogram-based
CatBoost	Excellent categorical handling, symmetric trees

Ensemble Strategies

1. Stacking: Logistic Regression meta-learner trained on out-of-fold predictions
2. Blending: Weighted average (LightGBM: 45%, CatBoost: 35%, XGBoost: 20%)

Project Structure

├── forest_cover_type_ensemble.ipynb  # Main notebook (run this)
├── stacking.py                       # Original Python script
├── train.csv                         # Training data
├── test-full.csv                     # Test data
├── full_submission.csv               # Sample submission format
├── submission_stacking_*.csv         # Stacking predictions
├── submission_blending_*.csv         # Blending predictions
└── README.md

Requirements

numpy
pandas
scikit-learn
lightgbm
xgboost
catboost
tqdm

Installation

pip install numpy pandas scikit-learn lightgbm xgboost catboost tqdm

Usage

Option 1: Jupyter Notebook (Recommended)

jupyter notebook forest_cover_type_ensemble.ipynb

Run cells sequentially. The notebook includes detailed explanations for each step.

Option 2: Python Script

python stacking.py

Configuration

Key parameters in the notebook/script:

N_FOLDS = 7      # Cross-validation folds
SEED = 42        # Random seed for reproducibility

Model hyperparameters are pre-tuned for optimal performance. Training takes approximately 15-30 minutes depending on hardware.

Results

The solution generates two submission files:

Method	Description
submission_stacking_*.csv	Meta-learner ensemble
submission_blending_*.csv	Weighted average ensemble

Submit both to Kaggle to compare leaderboard performance.

Potential Improvements

• Hyperparameter optimization with Optuna/GridSearch
• Additional models (ExtraTrees, Neural Networks)
• Feature selection using permutation importance
• Pseudo-labeling with high-confidence test predictions
• More aggressive feature interactions

References

• Kaggle Competition Page
• UCI ML Repository - Covertype Dataset

License

This project is for educational purposes as part of HEC coursework.