Model description
[More Information Needed]
Intended uses & limitations
[More Information Needed]
Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
<details> <summary> Click to expand </summary>
| Hyperparameter | Value |
|---|---|
| aggressive_elimination | False |
| cv | 5 |
| error_score | nan |
| estimator__categorical_features | |
| estimator__early_stopping | auto |
| estimator__l2_regularization | 0.0 |
| estimator__learning_rate | 0.1 |
| estimator__loss | auto |
| estimator__max_bins | 255 |
| estimator__max_depth | |
| estimator__max_iter | 100 |
| estimator__max_leaf_nodes | 31 |
| estimator__min_samples_leaf | 20 |
| estimator__monotonic_cst | |
| estimator__n_iter_no_change | 10 |
| estimator__random_state | |
| estimator__scoring | loss |
| estimator__tol | 1e-07 |
| estimator__validation_fraction | 0.1 |
| estimator__verbose | 0 |
| estimator__warm_start | False |
| estimator | HistGradientBoostingClassifier() |
| factor | 3 |
| max_resources | auto |
| min_resources | exhaust |
| n_jobs | -1 |
| param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
| random_state | 42 |
| refit | True |
| resource | n_samples |
| return_train_score | True |
| scoring | |
| verbose | 0 |
</details>
Model Plot
The model plot is below.
<style>#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce {color: black;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce pre{padding: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable {background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator:hover {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-item {z-index: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:only-child::after {width: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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 the default 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-text-repr-fallback {display: none;}</style><div id="sk-3de79340-4ee5-4aee-9c89-b3b7696153ce" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="474afc8c-e67d-430c-9432-eedced794614" type="checkbox" ><label for="474afc8c-e67d-430c-9432-eedced794614" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="cf1d66b1-cfe8-40b1-b6e9-7a62640add17" type="checkbox" ><label for="cf1d66b1-cfe8-40b1-b6e9-7a62640add17" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|---|
How to Get Started with the Model
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<details> <summary> Click to expand </summary>
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</details>
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