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# 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.**

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

StandardScaler()

LogisticRegression()

## 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

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