Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.
Richard M. SimonJyothi SubramanianMing-Chung LiSupriya MenezesPublished in: Briefings Bioinform. (2011)
Keyphrases
- predictive accuracy
- cross validation
- high dimensional data
- decision trees
- training set
- high predictive accuracy
- support vector
- nearest neighbor
- machine learning algorithms
- nearest neighbor classifiers
- unseen data
- model selection
- classification rules
- dimensionality reduction
- data sets
- variable selection
- high dimensional
- low dimensional
- regression problems
- hyperparameters
- prediction accuracy
- training data
- high dimensionality
- data points
- similarity search
- dimension reduction
- data analysis
- input space
- generalization error
- naive bayes
- classification algorithm
- input data
- feature selection
- rule sets
- k nearest neighbor
- training samples
- machine learning
- classification accuracy
- learning algorithm
- random forest
- survival data
- svm classifier
- real world
- logistic regression
- hyperplane
- training examples
- active learning
- ls svm
- survival analysis
- support vector machine
- feature set
- decision rules
- supervised learning