
# Simple example using plain scikit-learn

## Reproducing the model

Inside a Python environment, install the dependencies listed in `requirements.txt` and then run:

``` bash
python train.py
```

The resulting model artifact should be stored in `model.pickle`.

## The model

The used model is a simple logistic regression trained through gradient descent.

## Intended use & limitations

This model is just for demonstration purposes and should thus not be used.

## Dataset

The dataset is entirely synthetic and has no real world origin.

{"license": "bsd-3-clause", "tags": ["sklearn"], "datasets": ["synthetic dataset from sklearn"], "metrics": [{"type": "accuracy", "value": 0.948}]}
sklearn joblib license:bsd-3-clause region:us
TAGS
#sklearn #joblib #license-bsd-3-clause #region-us 


# Simple example using plain scikit-learn

## Reproducing the model

Inside a Python environment, install the dependencies listed in 'URL' and then run:



The resulting model artifact should be stored in 'URL'.

## The model

The used model is a simple logistic regression trained through gradient descent.

## Intended use & limitations

This model is just for demonstration purposes and should thus not be used.

## Dataset

The dataset is entirely synthetic and has no real world origin.

# Simple example using plain scikit-learn ## Reproducing the model

Inside a Python environment, install the dependencies listed in 'URL' and then run:



The resulting model artifact should be stored in 'URL'. ## The model

The used model is a simple logistic regression trained through gradient descent. ## Intended use & limitations

This model is just for demonstration purposes and should thus not be used. ## Dataset

The dataset is entirely synthetic and has no real world origin.
TAGS
#sklearn #joblib #license-bsd-3-clause #region-us 
 # Simple example using plain scikit-learn ## Reproducing the model

Inside a Python environment, install the dependencies listed in 'URL' and then run:



The resulting model artifact should be stored in 'URL'. ## The model

The used model is a simple logistic regression trained through gradient descent. ## Intended use & limitations

This model is just for demonstration purposes and should thus not be used. ## Dataset

The dataset is entirely synthetic and has no real world origin.
passage: TAGS
#sklearn #joblib #license-bsd-3-clause #region-us 
# Simple example using plain scikit-learn## Reproducing the model

Inside a Python environment, install the dependencies listed in 'URL' and then run:



The resulting model artifact should be stored in 'URL'.## The model

The used model is a simple logistic regression trained through gradient descent.## Intended use & limitations

This model is just for demonstration purposes and should thus not be used.## Dataset

The dataset is entirely synthetic and has no real world origin.