Supervised Learning

Last updated Feb 9, 2023 Edit Source

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

• 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/

See AI/Supervised Learning/Classification

See AI/Supervised Learning/Regression

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

See AI/Supervised Learning/Ensemble learning

See AI/Supervised Learning/Class imbalance

### Data augmentation See AI/Supervised Learning/Data augmentation

See AI/Supervised Learning/Model selection and tuning

• 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.