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firefighter
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.gitignore
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README.md
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Weather Data.csv
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model.ipynb
The FireFighter: A Wildfire Classification Model
Welcome! Shared using https://gitfront.io/, this is a private school project that isn't publicly listed on my GitHub profile. This is a recommended measure taken to prevent plagiarism, while still enabling me to share outside of my university -- hello, resume viewer! :)
Meet my wildfire classifier that uses hourly weather stats to classify whether a wildfire is likely to form under given conditions.
Table of Contents
Data Sourcing & Preprocessing
Regional focus was on North America.
Hourly weather statistics collected from Weather Underground.
Historical wildfires sourced from:
Data partitioned into an 80-20 train-test split.
Data features standardized using scikit-learn's StandardScaler.
Parameter Tuning
Relatively small dataset meant preventative measures were needed to address overfitting.
Resampling procedures: Bootstrapping, 5-fold cross-validation.
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1000 bootstrap samples were used to train and evaluate Logistic Regression & K-Nearest Neighbours (KNN) models.
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10 bootstrap samples were used to train and evaluate SVM (Support Vector Machine) model
- Bounded by computational restraints.
One-hot encoding was used for for categorical variables (eg. location, season).
Nominal encoding for our output (0 and 1 for "no" and "yes")
Results
Success was evaluated by computing F1 scores from confusion matrices for each model:
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83% F1 score for Logistic Regression
- P = 0.5 (default)
- Precision: 86%
- Recall: 81%
- Accuracy: 84%
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77% F1 score for Support Vector Machine (SVM)
- RBF kernel
- λ = 5 (regularization)
- γ = 0.001
- Precision: 84%
- Recall: 71%
- Accuracy: 79%
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74% F1 score for unscaled K-Nearest Neighbours (KNN)
- k = 1 (optimal MSE)
- Precision: 81%
- Recall: 68%
- Accuracy: 75%
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72% F1 score for scaled K-Nearest Neighbours (KNN)
- k = 1 (optimal MSE)
- Precision: 70%
- Recall: 74%
- Accuracy: 70%