class RegressionTree extends PredictorMat
The RegressionTree class implements a RegressionTree selecting splitting features
using minimal variance in children nodes. To avoid exponential choices in the selection,
supporting ordinal features currently. Use companion object is recommended for generate
Regression Tree.
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new
RegressionTree(x: MatriD, y: VectoD, fname_: Strings = null, hparam: HyperParameter = RegressionTree.hp, curDepth: Int = -1, branchValue: Int = -1, feature: Int = -1)
- x
the data vectors stored as rows of a matrix
- y
the response vector
- fname_
the names of the model's features/variables
- hparam
the hyper-parameters for the model
- curDepth
current depth
- branchValue
the branchValue for the tree node
- feature
the feature for the tree's parent node
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
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- Definition Classes
- Predictor
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def
buildTree(opt: (Int, Double)): Unit
Given the next most distinguishing feature/attribute, extend the regression tree.
Given the next most distinguishing feature/attribute, extend the regression tree.
- opt
the optimal feature and the variance
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def
clone(): AnyRef
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def
crossVal(k: Int, rando: Boolean): Unit
The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method.
The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method.
- k
the number of crosses and cross-validations (defaults to 10x).
- rando
flag for using randomized cross-validation
- Definition Classes
- RegressionTree → PredictorMat
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def
crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10, rando: Boolean = true): Array[Statistic]
- Definition Classes
- PredictorMat
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def
diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null, y_: VectoD = y): Unit
Given the error/residual vector, compute the quality of fit measures.
Given the error/residual vector, compute the quality of fit measures.
- e
the corresponding m-dimensional error vector (y - yp)
- w
the weights on the instances
- yp
the predicted response vector (x * b)
- Definition Classes
- Fit
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val
e: VectoD
- Attributes
- protected
- Definition Classes
- Predictor
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
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def
eval(xx: MatriD, yy: VectoD): Unit
Evaluate by diagnose the error.
Evaluate by diagnose the error.
- xx
the data matrix used in prediction
- yy
the actual response vector
- Definition Classes
- RegressionTree → PredictorMat → Predictor
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def
eval(): Unit
Evaluate by diagnose the error.
Evaluate by diagnose the error.
- Definition Classes
- RegressionTree → PredictorMat → Predictor
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def
fastThreshold(f: Int, x_f: VectoD, subSample: VectoI = null): Unit
Given feature 'f', use fast threshold selection to find an optimal threshold/ split point in O(NlogN) time.
Given feature 'f', use fast threshold selection to find an optimal threshold/ split point in O(NlogN) time.
- f
the given feature for which the threshold is desired
- x_f
column f in data matrix
- subSample
optional, use to select from the range
- See also
people.cs.umass.edu/~domke/courses/sml/12trees.pdf
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def
finalize(): Unit
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def
fit: VectoD
Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.
Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitLabel: Seq[String]
Return the labels for the quality of fit measures.
Return the labels for the quality of fit measures. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- Definition Classes
- Fit
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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var
fname: Strings
- Attributes
- protected
- Definition Classes
- PredictorMat
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final
def
getClass(): Class[_]
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- @native()
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def
hashCode(): Int
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def
hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
- Definition Classes
- PredictorMat
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val
index_rSq: Int
- Definition Classes
- Fit
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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val
k: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
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val
m: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
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def
mse_: Double
Return the mean of squares for error (sse / df._2).
Return the mean of squares for error (sse / df._2). Must call diagnose first.
- Definition Classes
- Fit
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
nextXY(f: Int, side: Int): (MatriD, VectoD)
Return new 'x' matrix and 'y' vector for next step of constructing regression tree.
Return new 'x' matrix and 'y' vector for next step of constructing regression tree.
- f
the feature index
- side
indicator for which side of child is chosen (i.e., 0 for left child)
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final
def
notify(): Unit
- Definition Classes
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- @native()
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final
def
notifyAll(): Unit
- Definition Classes
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- @native()
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def
parameter: VectoD
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
- Definition Classes
- Predictor
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def
predict(z: MatriD): VectorD
Given a data matrix z, predict the value by following the tree to the leaf.
Given a data matrix z, predict the value by following the tree to the leaf.
- z
the data matrix to predict
- Definition Classes
- RegressionTree → PredictorMat
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def
predict(z: VectoD): Double
Given a data vector z, predict the value by following the tree to the leaf.
Given a data vector z, predict the value by following the tree to the leaf.
- z
the data vector to predict
- Definition Classes
- RegressionTree → PredictorMat → Predictor
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def
predict(z: VectoI): Double
Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
- z
the vector to use for prediction
- Definition Classes
- Predictor
- def printT(nod: Node, level: Int): Unit
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def
printTree(): Unit
Print the regression tree in Pre-Order using 'printT' method.
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def
printTree2(): Unit
Print out the regression tree using Breadth First Search (BFS).
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def
reset(): Unit
Reset or re-initialize the frequency tables and the probability tables.
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def
resetDF(df_update: (Double, Double)): Unit
Reset the degrees of freedom to the new updated values.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
- df_update
the updated degrees of freedom
- Definition Classes
- Fit
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Predictor
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def
split(f: Int, thresh: Double): (Array[Int], Array[Int])
Split gives index of left and right child when spliting in 'thresh'.
Split gives index of left and right child when spliting in 'thresh'.
- f
the feature indicator
- thresh
threshold
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def
sumCoeff(b: VectoD, stdErr: VectoD = null): String
Produce the summary report portion for the cofficients.
Produce the summary report portion for the cofficients.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
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def
summary(): String
Compute and return summary diagostics for the regression model.
Compute and return summary diagostics for the regression model.
- Definition Classes
- PredictorMat
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def
summary(b: VectoD, stdErr: VectoD = null, show: Boolean = false): String
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
- b
the parameters/coefficients for the model
- show
flag indicating whether to print the summary
- Definition Classes
- Fit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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def
toString(): String
- Definition Classes
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def
train(interval: VectoI): Unit
Train the regression tree by selecting threshold for all the features in interval (subsamples).
Train the regression tree by selecting threshold for all the features in interval (subsamples).
- interval
only the values in interval will be used in selecting threshold
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def
train(yy: VectoD = y): RegressionTree
Train the regression tree by selecting threshold for all the features in 'yy' (can be used as all the samples or subsamples).
Train the regression tree by selecting threshold for all the features in 'yy' (can be used as all the samples or subsamples).
- yy
only the values in yy will be used in selecting threshold
- Definition Classes
- RegressionTree → PredictorMat → Predictor
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def
train(): PredictorMat
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
- Definition Classes
- PredictorMat
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def
train2(yy: VectoD = y): PredictorMat
- Definition Classes
- PredictorMat
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
- Definition Classes
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val
x: MatriD
- Attributes
- protected
- Definition Classes
- PredictorMat
-
val
y: VectoD
- Attributes
- protected
- Definition Classes
- PredictorMat