sklearn skops tabular-classification

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-9488cbf0-211e-46de-9d19-74e8624222b7 {color: black;background-color: white;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 pre{padding: 0;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-toggleable {background-color: white;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 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-9488cbf0-211e-46de-9d19-74e8624222b7 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-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-item {z-index: 1;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-parallel-item:only-child::after {width: 0;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 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-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-9488cbf0-211e-46de-9d19-74e8624222b7 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-9488cbf0-211e-46de-9d19-74e8624222b7 div.sk-text-repr-fallback {display: none;}</style><div id="sk-9488cbf0-211e-46de-9d19-74e8624222b7" class="sk-top-container" style="overflow: auto;"><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="871a72de-bae6-4105-ac5d-51538a192ff8" type="checkbox" ><label for="871a72de-bae6-4105-ac5d-51538a192ff8" 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="d6cb8281-9c7e-40ec-a4e2-3f8bc17cb5fa" type="checkbox" ><label for="d6cb8281-9c7e-40ec-a4e2-3f8bc17cb5fa" 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

Use the code below to get started with the model.

import joblib
import json
import pandas as pd
clf = joblib.load(skops-vory2y2w.pkl)
with open("config.json") as f:
    config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))

Model Card Authors

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Model Card Contact

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Citation

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