Machine learning models can largely outperform classical algorithms to make predictions about complexe problems, e.g. recognizing trees (which can vary a lot depending on the season, the species…). To do so, they learn from data (either from examples or experience) instead of following a well-defined sequence of instructions (like a cooking recipe). We humans do the same to teach our kids to recognize trees: we do not provide instructions but examples.
Explore the features
Learn the basics of the package here.
Explain a binary classifier with the Adult dataset
Explain a multi-class classifier with the Iris dataset.
Explain a regressor with the Boston dataset.
Explain an image classifier with the MNIST dataset.
Explain correlations in a multidimensional context.
Conduct real-world analyses
Explain the forecast for heart disease diagnosis.
Explain a classifier that predicts the presence of diabete.
Compare to alternatives
Partial Dependence Plot
Comparison with Partial Dependency Plot to understand marginal effect of variables.
Code source on GitHub
You can find onGitHub : theIntroduction, Installation guide, User guide (Measuring model influence, Evaluating model reliability, Support for image classification), Authors and License.
118 Rte de Narbonne
31400 Toulouse - FRANCE