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.

<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.

## 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.

<details> <summary> Click to expand </summary>

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

</details>

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

Hyperparameter search results

<details> <summary> Click to expand </summary>

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

</details>

Classification report

<details> <summary> Click to expand </summary>

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

</details>

Model description

Intended uses & limitations

Training Procedure

Hyperparameters

Model Plot

How to Get Started with the Model

Model Card Authors

Model Card Contact

Citation

Additional Content

Confusion matrix

Hyperparameter search results

Classification report

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