object bootstrap
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- def classification[M <: DataFrameClassifier](k: Int, formula: Formula, data: DataFrame)(trainer: (Formula, DataFrame) => M): ClassificationValidations[M]
Bootstrap validation on a data frame classifier.
Bootstrap validation on a data frame classifier.
- k
k-round bootstrap estimation.
- data
data samples.
- trainer
a code block to return a classifier trained on the given data.
- returns
the error rates of each round.
- def classification[T <: AnyRef, M <: Classifier[T]](k: Int, x: Array[T], y: Array[Int])(trainer: (Array[T], Array[Int]) => M): ClassificationValidations[M]
Bootstrap validation on a generic classifier.
Bootstrap validation on a generic classifier. The bootstrap is a general tool for assessing statistical accuracy. The basic idea is to randomly draw datasets with replacement from the training data, each sample the same size as the original training set. This is done many times (say k = 100), producing k bootstrap datasets. Then we refit the model to each of the bootstrap datasets and examine the behavior of the fits over the k replications.
- k
k-round bootstrap estimation.
- x
data samples.
- y
sample labels.
- trainer
a code block to return a classifier trained on the given data.
- returns
the error rates of each round.
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- def regression[M <: DataFrameRegression](k: Int, formula: Formula, data: DataFrame)(trainer: (Formula, DataFrame) => M): RegressionValidations[M]
Bootstrap validation on a data frame regression model.
Bootstrap validation on a data frame regression model.
- k
k-round bootstrap estimation.
- data
data samples.
- trainer
a code block to return a regression model trained on the given data.
- returns
the root mean squared error of each round.
- def regression[T <: AnyRef, M <: Regression[T]](k: Int, x: Array[T], y: Array[Double])(trainer: (Array[T], Array[Double]) => M): RegressionValidations[M]
Bootstrap validation on a generic regression model.
Bootstrap validation on a generic regression model.
- k
k-round bootstrap estimation.
- x
data samples.
- y
response variable.
- trainer
a code block to return a regression model trained on the given data.
- returns
the root mean squared error of each round.
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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.