README.md

metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_file: skops-bo_9fb88.pkl
widget:
  structuredData:
    area error:
      - 30.29
      - 96.05
      - 48.31
    compactness error:
      - 0.01911
      - 0.01652
      - 0.01484
    concave points error:
      - 0.01037
      - 0.0137
      - 0.01093
    concavity error:
      - 0.02701
      - 0.02269
      - 0.02813
    fractal dimension error:
      - 0.003586
      - 0.001698
      - 0.002461
    mean area:
      - 481.9
      - 1130
      - 748.9
    mean compactness:
      - 0.1058
      - 0.1029
      - 0.1223
    mean concave points:
      - 0.03821
      - 0.07951
      - 0.08087
    mean concavity:
      - 0.08005
      - 0.108
      - 0.1466
    mean fractal dimension:
      - 0.06373
      - 0.05461
      - 0.05796
    mean perimeter:
      - 81.09
      - 123.6
      - 101.7
    mean radius:
      - 12.47
      - 18.94
      - 15.46
    mean smoothness:
      - 0.09965
      - 0.09009
      - 0.1092
    mean symmetry:
      - 0.1925
      - 0.1582
      - 0.1931
    mean texture:
      - 18.6
      - 21.31
      - 19.48
    perimeter error:
      - 2.497
      - 5.486
      - 3.094
    radius error:
      - 0.3961
      - 0.7888
      - 0.4743
    smoothness error:
      - 0.006953
      - 0.004444
      - 0.00624
    symmetry error:
      - 0.01782
      - 0.01386
      - 0.01397
    texture error:
      - 1.044
      - 0.7975
      - 0.7859
    worst area:
      - 677.9
      - 1866
      - 1156
    worst compactness:
      - 0.2378
      - 0.2336
      - 0.2394
    worst concave points:
      - 0.1015
      - 0.1789
      - 0.1514
    worst concavity:
      - 0.2671
      - 0.2687
      - 0.3791
    worst fractal dimension:
      - 0.0875
      - 0.06589
      - 0.08019
    worst perimeter:
      - 96.05
      - 165.9
      - 124.9
    worst radius:
      - 14.97
      - 24.86
      - 19.26
    worst smoothness:
      - 0.1426
      - 0.1193
      - 0.1546
    worst symmetry:
      - 0.3014
      - 0.2551
      - 0.2837
    worst texture:
      - 24.64
      - 26.58
      - 26

Model description

[More Information Needed]

Intended uses & limitations

[More Information Needed]

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	log_loss
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)

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Evaluation Results

[More Information Needed]

How to Get Started with the Model

[More Information Needed]

Model Card Authors

This model card is written by following authors:

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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:

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