cs131-SKLL-practice

• scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. [Wikipedia]
• SKLL (pronounced “skull”) provides a number of utilities to make it simpler to run common scikit-learn experiments with pre-generated features. [SKLL website]

Preprocessing

First, write a code to clean up the dataset, using awk and shell scripting. (See mysplit.sh)

• Delimiter: Change semicolons to colons
• Label modification: Simplify the dataset so we can solve it as a binary classification problem
  • If the quality is >= 6, it is good wine = 1.
  • If <6, it is bad wine = 0.
• Create training and test datasets
  • 80% training
  • 20% test
• A clean dataset should look like:

SKLL Configuration

• SKLL tutorial
• SKLL configuration file
• List of learners available