object validate
- Alphabetic
- By Inheritance
- validate
- AnyRef
- Any
- Hide All
- Show All
- Public
- Protected
Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def classification[M <: DataFrameClassifier](formula: Formula, train: DataFrame, test: DataFrame)(trainer: (Formula, DataFrame) => M): ClassificationValidation[M]
Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
- train
training data.
- test
test data.
- trainer
a code block to return a classifier trained on the given data.
- returns
the trained classifier.
- def classification[T <: AnyRef, M <: Classifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int])(trainer: (Array[T], Array[Int]) => M): ClassificationValidation[M]
Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
- T
the type of training and test data.
- x
training data.
- y
training labels.
- testx
test data.
- testy
test data labels.
- trainer
a code block to return a classifier trained on the given data.
- returns
the trained classifier.
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- def regression[M <: DataFrameRegression](formula: Formula, train: DataFrame, test: DataFrame)(trainer: (Formula, DataFrame) => M): RegressionValidation[M]
Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
- train
training data.
- test
test data.
- trainer
a code block to return a classifier trained on the given data.
- returns
the trained classifier.
- def regression[T <: AnyRef, M <: Regression[T]](x: Array[T], y: Array[Double], testx: Array[T], testy: Array[Double])(trainer: (Array[T], Array[Double]) => M): RegressionValidation[M]
Test a generic classifier.
Test a generic classifier. The accuracy will be measured and printed out on standard output.
- T
the type of training and test data.
- x
training data.
- y
training labels.
- testx
test data.
- testy
test data labels.
- trainer
a code block to return a classifier trained on the given data.
- returns
the trained classifier.
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def toString(): String
- Definition Classes
- AnyRef → Any
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
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.