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Model description

This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth.

Intended uses & limitations

This model is not ready to be used in production.

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

Click to expand
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

Model Plot

The model plot is below.

HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)
Please rerun this cell to show the HTML repr or trust the notebook.

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric Value
accuracy 0.959064
f1 score 0.959064

How to Get Started with the Model

Use the code below to get started with the model.

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import pickle
with open(pkl_filename, 'rb') as file:
    clf = pickle.load(file)

Model Card Authors

This model card is written by following authors:

skops_user

Model Card Contact

You can contact the model card authors through following channels: [More Information Needed]

Citation

Below you can find information related to citation.

BibTeX:

bibtex
@inproceedings{...,year={2020}}

Additional Content

Confusion matrix

Confusion matrix

Hyperparameter search results

Click to expand
iter n_resources mean_fit_time std_fit_time mean_score_time std_score_time param_max_depth param_max_leaf_nodes params split0_test_score split1_test_score split2_test_score split3_test_score split4_test_score mean_test_score std_test_score rank_test_score split0_train_score split1_train_score split2_train_score split3_train_score split4_train_score mean_train_score std_train_score
0 44 0.0498069 0.0107112 0.0121156 0.0061838 2 5 {'max_depth': 2, 'max_leaf_nodes': 5} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0492636 0.0187271 0.00738611 0.00245441 2 10 {'max_depth': 2, 'max_leaf_nodes': 10} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0572055 0.0153176 0.0111395 0.0010297 2 15 {'max_depth': 2, 'max_leaf_nodes': 15} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0498482 0.0177091 0.00857358 0.00415935 5 5 {'max_depth': 5, 'max_leaf_nodes': 5} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0500658 0.00992094 0.00998321 0.00527031 5 10 {'max_depth': 5, 'max_leaf_nodes': 10} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0525903 0.0151616 0.00874681 0.00462998 5 15 {'max_depth': 5, 'max_leaf_nodes': 15} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0512018 0.0130152 0.00881834 0.00500514 10 5 {'max_depth': 10, 'max_leaf_nodes': 5} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.0566921 0.0186051 0.00513492 0.000498488 10 10 {'max_depth': 10, 'max_leaf_nodes': 10} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
0 44 0.060587 0.04041 0.00987453 0.00529624 10 15 {'max_depth': 10, 'max_leaf_nodes': 15} 0.875 0.5 0.625 0.75 0.375 0.625 0.176777 5 0.628571 0.628571 0.628571 0.514286 0.514286 0.582857 0.0559883
1 132 0.232459 0.0479878 0.0145514 0.00856422 10 5 {'max_depth': 10, 'max_leaf_nodes': 5} 0.961538 0.923077 0.923077 0.961538 0.961538 0.946154 0.0188422 2 1 1 1 1 1 1 0
1 132 0.272297 0.0228833 0.011561 0.0068272 10 10 {'max_depth': 10, 'max_leaf_nodes': 10} 0.961538 0.923077 0.923077 0.961538 0.961538 0.946154 0.0188422 2 1 1 1 1 1 1 0
1 132 0.239161 0.0330412 0.0116591 0.003554 10 15 {'max_depth': 10, 'max_leaf_nodes': 15} 0.961538 0.923077 0.923077 0.961538 0.961538 0.946154 0.0188422 2 1 1 1 1 1 1 0
2 396 0.920334 0.18198 0.0166654 0.00776263 10 15 {'max_depth': 10, 'max_leaf_nodes': 15} 0.962025 0.911392 0.987342 0.974359 0.935897 0.954203 0.0273257 1 1 1 1 1 1 1 0

Classification report

Click to expand
index precision recall f1-score support
malignant 0.951613 0.936508 0.944 63
benign 0.963303 0.972222 0.967742 108
macro avg 0.957458 0.954365 0.955871 171
weighted avg 0.958996 0.959064 0.958995 171
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