README.md

metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
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	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

How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

[More Information Needed]

Model Card Authors

This model card is written by following authors:

[More Information Needed]

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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