metadata

library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
model_file: model.pkl
widget:
  structuredData:
    area_mean:
      - 407.4
      - 1335
      - 428
    area_se:
      - 26.99
      - 77.02
      - 17.12
    area_worst:
      - 508.9
      - 1946
      - 546.3
    compactness_mean:
      - 0.05991
      - 0.1076
      - 0.069
    compactness_se:
      - 0.01065
      - 0.01895
      - 0.01727
    compactness_worst:
      - 0.1049
      - 0.3055
      - 0.188
    concave points_mean:
      - 0.02069
      - 0.08941
      - 0.01393
    concave points_se:
      - 0.009175
      - 0.01232
      - 0.006747
    concave points_worst:
      - 0.06544
      - 0.2112
      - 0.06913
    concavity_mean:
      - 0.02638
      - 0.1527
      - 0.02669
    concavity_se:
      - 0.01245
      - 0.02681
      - 0.02045
    concavity_worst:
      - 0.08105
      - 0.4159
      - 0.1471
    fractal_dimension_mean:
      - 0.05934
      - 0.05478
      - 0.06057
    fractal_dimension_se:
      - 0.001461
      - 0.001711
      - 0.002922
    fractal_dimension_worst:
      - 0.06487
      - 0.07055
      - 0.07993
    perimeter_mean:
      - 73.28
      - 134.8
      - 75.51
    perimeter_se:
      - 2.684
      - 4.119
      - 1.444
    perimeter_worst:
      - 83.12
      - 166.8
      - 85.22
    radius_mean:
      - 11.5
      - 20.64
      - 11.84
    radius_se:
      - 0.3927
      - 0.6137
      - 0.2222
    radius_worst:
      - 12.97
      - 25.37
      - 13.3
    smoothness_mean:
      - 0.09345
      - 0.09446
      - 0.08871
    smoothness_se:
      - 0.00638
      - 0.006211
      - 0.005517
    smoothness_worst:
      - 0.1183
      - 0.1562
      - 0.128
    symmetry_mean:
      - 0.1834
      - 0.1571
      - 0.1533
    symmetry_se:
      - 0.02292
      - 0.01276
      - 0.01616
    symmetry_worst:
      - 0.274
      - 0.2689
      - 0.2535
    texture_mean:
      - 18.45
      - 17.35
      - 18.94
    texture_se:
      - 0.8429
      - 0.6575
      - 0.8652
    texture_worst:
      - 22.46
      - 23.17
      - 24.99

Model description

This is a Logistic Regression trained on breast cancer dataset.

Intended uses & limitations

This model is trained for educational purposes.

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

Click to expand

Hyperparameter	Value
memory
steps	[('scaler', StandardScaler()), ('model', LogisticRegression())]
verbose	False
scaler	StandardScaler()
model	LogisticRegression()
scaler__copy	True
scaler__with_mean	True
scaler__with_std	True
model__C	1.0
model__class_weight
model__dual	False
model__fit_intercept	True
model__intercept_scaling	1
model__l1_ratio
model__max_iter	100
model__multi_class	auto
model__n_jobs
model__penalty	l2
model__random_state
model__solver	lbfgs
model__tol	0.0001
model__verbose	0
model__warm_start	False

Model Plot

The model plot is below.

Pipeline(steps=[('scaler', StandardScaler()), ('model', LogisticRegression())])

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	0.965035
f1 score	0.965035

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(model.pkl)
with open("config.json") as f:
    config = json.load(f)
clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))

scikit-learn
/

blog-example