case class LogRegModel(weights: List[FeatureWeight], intercept: Double) extends Model with Product with Serializable
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Instance Constructors
- new LogRegModel(weights: List[FeatureWeight], intercept: Double)
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- final def eq(arg0: AnyRef): Boolean
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- def eval(data: Dataset, metric: Metric): Double
Eval a metric over the whole dataset
Eval a metric over the whole dataset
- Definition Classes
- LogRegModel → Model
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
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- @native() @HotSpotIntrinsicCandidate()
- val intercept: Double
- final def isInstanceOf[T0]: Boolean
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- final def notify(): Unit
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- final def notifyAll(): Unit
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- def predict(values: RealVector): Double
Make single prediction
Make single prediction
- Definition Classes
- LogRegModel → Model
- def predict(values: RealMatrix): ArrayRealVector
Make batch prediction, default impl falling back to per-row predict.
Make batch prediction, default impl falling back to per-row predict. You should overload it for better performance.
- Definition Classes
- Model
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- final def synchronized[T0](arg0: => T0): T0
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- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(arg0: Long): Unit
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- final def wait(): Unit
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- val weights: List[FeatureWeight]
- val weightsVector: ArrayRealVector
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- def finalize(): Unit
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