--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: skops model_file: clf.skops widget: - structuredData: x0: - 25.861774444580078 - 13.925846099853516 - 18.626529693603516 x1: - -9.131807327270508 - -9.77347183227539 - -9.504095077514648 x10: - 1.9384498596191406 - 2.884918212890625 - 2.2260468006134033 x11: - 3.0784831047058105 - 3.9418864250183105 - 4.06421422958374 x12: - 2.991974353790283 - 2.9264330863952637 - 2.5255069732666016 x13: - 2.345289707183838 - 2.3997442722320557 - 2.200080394744873 x14: - 1.7882720232009888 - 1.840790867805481 - 1.383643388748169 x15: - 1.6710506677627563 - 1.2678987979888916 - 0.47583726048469543 x16: - 1.840790867805481 - 1.7882720232009888 - 1.0316907167434692 x17: - 2.3997442722320557 - 2.345289707183838 - 1.8518061637878418 x18: - 1.380855917930603 - 1.2926031351089478 - 1.0395294427871704 x19: - 1.241168737411499 - 1.126420021057129 - 0.8134236931800842 x2: - 4.62739896774292 - 5.171527862548828 - 4.921814441680908 x20: - 0.9832149744033813 - 0.8152679800987244 - 0.38093870878219604 x21: - 0.8598455786705017 - 0.6651478409767151 - 0.17098481953144073 x22: - 0.9832149744033813 - 0.8152679800987244 - 0.38093870878219604 x23: - 1.241168737411499 - 1.126420021057129 - 0.8134236931800842 x24: - 2.725480556488037 - 3.022055149078369 - 3.3232314586639404 x25: - 1.8365917205810547 - 2.0849626064300537 - 2.1735572814941406 x26: - 1.22439444065094 - 1.251629114151001 - 1.4565647840499878 x3: - 0.15449941158294678 - 0.03677806630730629 - 0.07167093455791473 x4: - -0.024682553485035896 - -0.02837284840643406 - -0.029335789382457733 x5: - -0.5647724866867065 - -0.19825565814971924 - -0.3138836622238159 x6: - 4.030393123626709 - 5.093674182891846 - 5.913875102996826 x7: - 3.9418864250183105 - 3.0784831047058105 - 3.5550312995910645 x8: - 2.884918212890625 - 1.9384498596191406 - 1.8467140197753906 x9: - 1.4331213235855103 - 1.9454820156097412 - 0.7261717319488525 --- # 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 |
### Model Plot
LGBMClassifier(n_estimators=200)
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## Evaluation Results Metrics calculated on a validation set of 1% of the test tiles | Metric | Value | |-----------|----------| | accuracy | 0.87635 | | precision | 0.785237 | | recall | 0.756414 | | f1 | 0.770556 | ## Dataset description https://www.geolion.zh.ch/geodatensatz/2831 ## Preprocessing description Images are resampled to 50 cm resolution. Train/test split based on image descriptors with 1% of tiles selected for training. # How to Get Started with the Model [More Information Needed] # Model Card Authors Martí Bosch # Model Card Contact marti.bosch@epfl.ch # Citation https://joss.theoj.org/papers/10.21105/joss.02172 # Example predictions
Click to expand ![Example predictions](plot.png)