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Simplified analysis of sklearn datasets

Project description

The skippy python package

Skip the boilerplate of scikit-learn machine learning examples.


pip install skippy


In a shell environment, you can run skippy with no arguments to perform a Logistic Regression on the digits dataset.

This will produce a 10 x 10 confusion matrix with the Accuracy Score at the top.

You can also pass arguments to skippy at the command line.

For example,

skippy -data diabetes -type linear_model -name Lasso
# Or
skippy -d diabetes -t linear_model -n Lasso

will run a linear regression with lasso regularization (L1) on the diabetes dataset.

The data argument can be any of the following built-in scikit-learn datasets:

  • Regression
    • boston
    • diabetes
  • Classification
    • digits
    • iris
    • wine
    • breast_cancer

The type and name arguments are referring to the model type and name from scikit-learn. The type is the submodule, e.g.

  • linear_model
  • naive_bayes
  • ensemble
  • svm

while the name is the what is actually imported, e.g.

  • LinearRegression
  • GaussianNB
  • RandomForestRegressor
  • SVC

Simplify code to a single function call per step:

from sklearn.metrics import confusion_matrix, accuracy_score
import skippy as skp

data = skp.get_data('digits')
x_train, x_test, y_train, y_test = skp.split_data(data)

model = skp.get_model(model_type='ensemble',

fit =, y_train)
skp.pickle_model(filename='digits_rf.pickle', model=fit)
predictions = fit.predict(x_test)

confmat = confusion_matrix(y_true=y_test, y_pred=predictions)
accuracy = accuracy_score(y_true=y_test, y_pred=predictions)


Or run a whole pipeline with one function:

import skippy as skp


For inspiration, look at the example pipelines in the pipelines folder of the skippy repo.

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