| | --- |
| | 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 |
| |
|
| | <details> |
| | <summary> Click to expand </summary> |
| |
|
| | | 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 | |
| | |
| | </details> |
| | |
| | ### Model Plot |
| | |
| | <style>#sk-container-id-15 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;} |
| | }#sk-container-id-15 {color: var(--sklearn-color-text); |
| | }#sk-container-id-15 pre {padding: 0; |
| | }#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; |
| | }#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); |
| | }#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative; |
| | }#sk-container-id-15 div.sk-text-repr-fallback {display: none; |
| | }div.sk-parallel-item, |
| | div.sk-serial, |
| | div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; |
| | }/* Parallel-specific style estimator block */#sk-container-id-15 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; |
| | }#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; |
| | }#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column; |
| | }#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; |
| | }#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; |
| | }#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0; |
| | }/* Serial-specific style estimator block */#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; |
| | }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is |
| | clickable and can be expanded/collapsed. |
| | - Pipeline and ColumnTransformer use this feature and define the default style |
| | - Estimators will overwrite some part of the style using the `sk-estimator` class |
| | *//* Pipeline and ColumnTransformer style (default) */#sk-container-id-15 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background); |
| | }/* Toggleable label */ |
| | #sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center; |
| | }#sk-container-id-15 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); |
| | }#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); |
| | }/* Toggleable content - dropdown */#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
| | }#sk-container-id-15 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
| | }#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
| | }#sk-container-id-15 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); |
| | }#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; |
| | }#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; |
| | }/* Pipeline/ColumnTransformer-specific style */#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); |
| | }#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); |
| | }/* Estimator-specific style *//* Colorize estimator box */ |
| | #sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
| | }#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
| | }#sk-container-id-15 div.sk-label label.sk-toggleable__label, |
| | #sk-container-id-15 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); |
| | }/* On hover, darken the color of the background */ |
| | #sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); |
| | }/* Label box, darken color on hover, fitted */ |
| | #sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); |
| | }/* Estimator label */#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; |
| | }#sk-container-id-15 div.sk-label-container {text-align: center; |
| | }/* Estimator-specific */ |
| | #sk-container-id-15 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
| | }#sk-container-id-15 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
| | }/* on hover */ |
| | #sk-container-id-15 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
| | }#sk-container-id-15 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
| | }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, |
| | a:link.sk-estimator-doc-link, |
| | a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1); |
| | }.sk-estimator-doc-link.fitted, |
| | a:link.sk-estimator-doc-link.fitted, |
| | a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
| | }/* On hover */ |
| | div.sk-estimator:hover .sk-estimator-doc-link:hover, |
| | .sk-estimator-doc-link:hover, |
| | div.sk-label-container:hover .sk-estimator-doc-link:hover, |
| | .sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
| | }div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, |
| | .sk-estimator-doc-link.fitted:hover, |
| | div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, |
| | .sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
| | }/* Span, style for the box shown on hovering the info icon */ |
| | .sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3); |
| | }.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); |
| | }.sk-estimator-doc-link:hover span {display: block; |
| | }/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-15 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; |
| | }#sk-container-id-15 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
| | }/* On hover */ |
| | #sk-container-id-15 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
| | }#sk-container-id-15 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); |
| | } |
| | </style><div id="sk-container-id-15" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LGBMClassifier(n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" checked><label for="sk-estimator-id-15" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> LGBMClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>LGBMClassifier(n_estimators=200)</pre></div> </div></div></div></div> |
| | |
| | ## 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 |
| | |
| | [email protected] |
| | |
| | # Citation |
| | |
| | https://joss.theoj.org/papers/10.21105/joss.02172 |
| | |
| | # Example predictions |
| | |
| | <details> |
| | <summary> Click to expand </summary> |
| | |
| |  |
| | |
| | </details> |
| | |
