scikit-learn/blog-example

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"]))

Additional Content

Confusion Matrix