Spatial ML
Supervised
Classification problems
Regression problems
…
Models
Linear regression
Logistic regression
Decision trees
Random forest
Gradient-boosted trees
Neural networks
Workflow
Split to train and test parts
Fit the model
Evaluate
(data standardisation)
Hyper-parameter tuning
Data augmentation
Evaluation methods
Classification
Confusion matrix
| Cat | Dog | Ant | Fly | |
|---|---|---|---|---|
| Cat | 45 | 5 | 2 | 0 |
| Dog | 3 | 40 | 5 | 2 |
| Ant | 1 | 2 | 38 | 4 |
| Fly | 0 | 1 | 3 | 46 |
Evaluation methods
Classification
Confusion matrix
Accuracy
Cohen’s kappa score
Precision
Recall
…
Spatial dimension in ML
Spatial feature engineering
Map synthesis
Proximity
Map matching
Spatial effects
Dependence
Heterogeneity
import sklearn
