This tutorial will guide towards good evaluation of machine-learning models, choosing metrics and procedures that match the intended usage, with code examples using the latest scikit-learn's features. We will discuss how good metrics should characterize all aspects of error, e.g. on the positive and negative class; the probability of a detection, or the probability of a true event given a detection; as they may need to catter for class imbalance. Metrics may also evaluate confidence scores, e.g. calibration. Model-evaluation procedures should gauge not only the expected generalization performance, but also its variations.

This tutorial will provide a beginner introduction to scikit-learn. Scikit-learn is a Python package for machine learning.

This tutorial will be subdivided into three parts. First, we will present how to design a predictive modeling pipeline that deals with heterogeneous types of data. Then, we will go more into detail in the evaluation of models and the type of trade-off to consider. Finally, we will show how to tune the hyperparameters of the pipeline.

This tutorial will provide a beginner introduction to scikit-learn. Scikit-learn is a Python package for machine learning.

This tutorial will be subdivided into three parts. First, we will present how to design a predictive modeling pipeline that deals with heterogeneous types of data. Then, we will go more into detail in the evaluation of models and the type of trade-off to consider. Finally, we will show how to tune the hyperparameters of the pipeline.