object loocv
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- loocv
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- def classification(formula: Formula, data: DataFrame)(trainer: (Formula, DataFrame) => DataFrameClassifier): ClassificationMetrics
Leave-one-out cross validation on a data frame classifier.
Leave-one-out cross validation on a data frame classifier.
- formula
model formula.
- data
data samples.
- trainer
a code block to return a classifier trained on the given data.
- returns
metric scores.
- def classification[T <: AnyRef](x: Array[T], y: Array[Int])(trainer: (Array[T], Array[Int]) => Classifier[T]): ClassificationMetrics
Leave-one-out cross validation on a generic classifier.
Leave-one-out cross validation on a generic classifier. LOOCV uses a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. This is the same as a K-fold cross-validation with K being equal to the number of observations in the original sample. Leave-one-out cross-validation is usually very expensive from a computational point of view because of the large number of times the training process is repeated.
- x
data samples.
- y
sample labels.
- trainer
a code block to return a classifier trained on the given data.
- returns
metric scores.
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- def regression(formula: Formula, data: DataFrame)(trainer: (Formula, DataFrame) => DataFrameRegression): RegressionMetrics
Leave-one-out cross validation on a data frame regression model.
Leave-one-out cross validation on a data frame regression model.
- formula
model formula.
- data
data samples.
- trainer
a code block to return a regression model trained on the given data.
- returns
metric scores.
- def regression[T <: AnyRef](x: Array[T], y: Array[Double])(trainer: (Array[T], Array[Double]) => Regression[T]): RegressionMetrics
Leave-one-out cross validation on a generic regression model.
Leave-one-out cross validation on a generic regression model.
- x
data samples.
- y
response variable.
- trainer
a code block to return a regression model trained on the given data.
- returns
metric scores.
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Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.