Supervised Learning

Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs

Resources

• https://en.wikipedia.org/wiki/Supervised_learning
• Supervised Learning cheatsheet
• http://scikit-learn.org/stable/supervised_learning.html
• Metrics:
  • http://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html
  • ROC curves, AUC
    • http://www.dataschool.io/roc-curves-and-auc-explained/
    • http://corysimon.github.io/articles/what-is-an-roc-curve/

Classification

See Classification

Regression

See Regression

Structured learning

• https://en.wikipedia.org/wiki/Structured_prediction

Ensemble learning

See Ensemble learning

Class imbalance

See Class imbalance

Data augmentation

See Data augmentation

Model selection and tuning

See Model selection and tuning

Model validation and drift

See Model validation and drift

Probability calibration

• https://en.wikipedia.org/wiki/Calibration_(statistics)
• https://scikit-learn.org/stable/modules/calibration.html
• When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you some kind of confidence on the prediction. Some models can give you poor estimates of the class probabilities and some even do not support probability prediction.
• Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class.
• https://scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html
• Isotonic regression:
  • https://scikit-learn.org/stable/modules/isotonic.html
  • Isotonic regression is a probability calibration technique which can calibrate classifier scores to approximate probability values by fitting a stepwise non-decreasing function along the scores returned by the classifier.