This Python package provides command-line utilities to make it easier to run machine learning experiments with scikit-learn. One of the primary goals of our project is to make it so that you can run scikit-learn experiments without actually needing to write any code other than what you used to generate/extract the features.

You can install using either pip or conda. See details here.

• Python 3.10, 3.11, or 3.12.
• beautifulsoup4
• gridmap (only required if you plan to run things in parallel on a DRMAA-compatible cluster)
• joblib
• pandas
• ruamel.yaml
• scikit-learn
• seaborn
• tabulate

The main utility we provide is called run_experiment and it can be used to easily run a series of learners on datasets specified in a configuration file like:

[General]
experiment_name = Titanic_Evaluate_Tuned
# valid tasks: cross_validate, evaluate, predict, train
task = evaluate

[Input]
# these directories could also be absolute paths
# (and must be if you're not running things in local mode)
train_directory = train
test_directory = dev
# Can specify multiple sets of feature files that are merged together automatically
featuresets = [["family.csv", "misc.csv", "socioeconomic.csv", "vitals.csv"]]
# List of scikit-learn learners to use
learners = ["RandomForestClassifier", "DecisionTreeClassifier", "SVC", "MultinomialNB"]
# Column in CSV containing labels to predict
label_col = Survived
# Column in CSV containing instance IDs (if any)
id_col = PassengerId

[Tuning]
# Should we tune parameters of all learners by searching provided parameter grids?
grid_search = true
# Function to maximize when performing grid search
objectives = ['accuracy']

[Output]
# Also compute the area under the ROC curve as an additional metric
metrics = ['roc_auc']
# The following can also be absolute paths
logs = output
results = output
predictions = output
probability = true
models = output

For more information about getting started with run_experiment, please check out our tutorial, or our config file specs.

You can also follow this interactive Jupyter tutorial.

We also provide utilities for:

• converting between machine learning toolkit formats (e.g., ARFF, CSV)
• filtering feature files
• joining feature files
• other common tasks

If you just want to avoid writing a lot of boilerplate learning code, you can also use our simple Python API which also supports pandas DataFrames. The main way you'll want to use the API is through the Learner and Reader classes. For more details on our API, see the documentation.

While our API can be broadly useful, it should be noted that the command-line utilities are intended as the primary way of using SKLL. The API is just a nice side-effect of our developing the utilities.

SciKit-Learn Laboratory (SKLL) is pronounced "skull": that's where the learning happens.

• Simpler Machine Learning with SKLL 1.0, Dan Blanchard, PyData NYC 2014 (video | slides)
• Simpler Machine Learning with SKLL, Dan Blanchard, PyData NYC 2013 (video | slides)

If you are using SKLL in your work, you can cite it as follows: "We used scikit-learn (Pedragosa et al, 2011) via the SKLL toolkit (https://github.com/EducationalTestingService/skll)."

SKLL is featured in Data Science at the Command Line by Jeroen Janssens.

See GitHub releases.

Thank you for your interest in contributing to SKLL! See CONTRIBUTING.md for instructions on how to get started.