Episode 11: Hyperparameter Tuning for Geospatial Machine Studying Fashions
Introduction
Even with nice information and options, a machine studying mannequin received’t carry out at its greatest with out tuning the knobs behind the scenes — the hyperparameters.
On this episode, we discover hyper-parameter tuning, a vital step in maximizing mannequin efficiency when working with geospatial information. 🌍🔧
Hyperparameters are configurations exterior to the mannequin that have an effect on the way it learns.
Examples:
n_estimatorsin Random ForestCandgammain SVMlearning_ratein Gradient Boosting
These should be set earlier than coaching and considerably affect mannequin habits.
⚠️ Default settings are hardly ever optimum.
Tuning helps:
✅ Maximize accuracy
✅ Keep away from underfitting/overfitting
✅ Enhance generalization
Particularly in geospatial ML, the place information might be complicated and noisy, tuning ensures fashions adapt successfully to spatial heterogeneity.
📌 Grid Search
Tries each mixture of specified hyperparameters.
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier param_grid = {'n_estimators': [50, 100], 'max_depth': [10, 20]}
grid = GridSearchCV(RandomForestClassifier(), param_grid, cv=3)
grid.match(X_train, y_train)
print(grid.best_params_)
📌 Randomized Search
Pattern combos randomly kind the parameter grid quicker and are sometimes simply as efficient.
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint param_dist = {'n_estimators': randint(50, 150), 'max_depth': randint(5, 30)}
rand_search = RandomizedSearchCV(RandomForestClassifier(), param_dist, n_iter=10, cv=3)
rand_search.match(X_train, y_train)
📌 Bayesian Optimization & AutoML (Superior)
Use prior outcomes to resolve the subsequent greatest mixture — environment friendly and good!
✅ Use spatial cross-validation to keep away from spatial bias
✅ Monitor coaching time (geospatial information might be heavy)
✅ Consider with spatial metrics, not simply accuracy
Hyperparameter tuning is a must-do step for any severe geospatial ML workflow. It turns an “okay” mannequin right into a high-performing one.
