This course material presents some modules and classes of scikit-learn, a library for machine learning in Python.
We focused on a typical classification process as a first step: the subdivision of the dataset into training and test sets; the learning of the logistic regression on the training sample; applying the model to the test set in order to obtain the predicted class values; the evaluation of the classifier using the confusion matrix and the calculation of the performance measurements.
In the second step, we study other important domains of the classification task: the cross-validation error evaluation when we deal with a small dataset; the scoring process for direct marketing; the grid search for detecting the optimal parameters of algorithms for a given dataset; the feature selection issue.
Keywords: python, numpy, pandas, scikit-learn, logistic regression, predictive analytics
Slides: Machine Learning with scikit-learn
Dataset and programs: scikit-learn - Programs and dataset
References :
"scikit-learn -- Machine Learning in Python" on scikit-learn.org
Python - Official Site
