Supervised machine learning [Neurosurgery Wiki]

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====== Supervised machine learning ======

Classification: These algorithms are used to predict categorical labels or classes based on input features. Common classification algorithms include:

Logistic Regression
Decision Trees
Random Forest
Support Vector Machines (SVM)
k-Nearest Neighbors (k-NN)
Naive Bayes
Neural Networks
Regression: In regression, algorithms predict continuous numerical values instead of discrete classes. Popular regression algorithms include:

Linear Regression
Ridge and Lasso Regression
Support Vector Regression (SVR)
Decision Trees for Regression
Random Forest Regression
Gradient Boosting Regressors (e.g., XGBoost, LightGBM)
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[[Supervised learning]] has been employed in neurosurgical [[diagnosis]], presurgical [[planning]], and outcome prediction. Machine learning has been used to characterize performance during otolaryngology and dental
VR procedures
((Rhienmora P, Haddawy P, Khanal P, Suebnukarn S, Dailey MN
(2010) A virtual reality simulator for teaching and evaluating dental
procedures. Methods Inf Med 49(04):396–405))
((Kerwin T, Wiet G, Stredney D, Shen HW (2012) Automatic scoring
of virtual mastoidectomies using expert examples. Int J Comput
Assist Radiol Surg 7(1):1–11))
((Ma X, Wijewickrema S, Zhou S, Zhou Y, Mhammedi Z, O'Leary S,
et al. Adversarial generation of real-time feedback with neural networks for simulation-based training. arXiv preprint:1703.01460.
2017 Mar 4))
((Ma X, Wijewickrema S, Zhou Y, Zhou S, O’Leary S, Bailey J
(2017) Providing effective real-time feedback in simulation-based
surgical training. In: International Conference on Medical Image
Computing and Computer-Assisted Intervention. Springer, Cham,
pp 566–574))
((Wijewickrema S, Ma X, Piromchai P, Piromchai P, Briggs R, James
BJ et al (2018) Providing automated real-time technical feedback
for virtual reality based surgical training: is the simpler the better?
In: International Conference on Artificial Intelligence in Education.
Springer, pp 584–598, Cham))
((Sewell C, Morris D, Blevins NH, Dutta S, Agrawal S, Federico
Barbagli F et al (2008) Providing metrics and performance feedback in a surgical simulator. Comput Aided Surg 13(2):63–81))

This [[algorithm]] consist of a [[target]] / [[outcome]] variable (or dependent variable) which is to be predicted from a given set of [[predictor]]s (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: [[Regression]], [[Decision Tree,]] Random Forest, KNN, [[Logistic Regression]] etc.

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

It infers a function from labeled training data consisting of a set of training examples.

In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the [[training data]] and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).

The parallel task in human and animal psychology is often referred to as concept learning.
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===== Neurosurgery =====
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