---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: clf.skops
widget:
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---
# Model description
LightGBM classifier of tree/non-tree pixels from aerial imagery trained on Zurich's Orthofoto Sommer 2014/15 using detectree.
## Intended uses & limitations
Segment tree/non-tree pixels from aerial imagery
## Training Procedure
[More Information Needed]
### Hyperparameters
Click to expand
| Hyperparameter | Value |
|-------------------|---------|
| boosting_type | gbdt |
| class_weight | |
| colsample_bytree | 1.0 |
| importance_type | split |
| learning_rate | 0.1 |
| max_depth | -1 |
| min_child_samples | 20 |
| min_child_weight | 0.001 |
| min_split_gain | 0.0 |
| n_estimators | 200 |
| n_jobs | |
| num_leaves | 31 |
| objective | |
| random_state | |
| reg_alpha | 0.0 |
| reg_lambda | 0.0 |
| subsample | 1.0 |
| subsample_for_bin | 200000 |
| subsample_freq | 0 |
LGBMClassifier(n_estimators=200)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LGBMClassifier(n_estimators=200)