class RegressionTree_GB extends PredictorMat
The RegressionTree_GB class uses Gradient Boosting on RegressionTree.
One Tree is included in the model at a time wisely chosen for reducing gradient.
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new
RegressionTree_GB(x: MatriD, y: VectoD, fname_: Strings = null, hparam: HyperParameter = RegressionTree_GB.hp)
- x
the data vectors stored as rows of a matrix
- y
the response vector
- fname_
the feature/variable names
- hparam
the hyper-parameters for the model
Value Members
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def
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val
b: VectoD
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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_GB → 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
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- Predictor
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
eval(xx: MatriD, yy: VectoD): Unit
Compute the error and useful diagnostics for the test dataset.
Compute the error and useful diagnostics for the test dataset.
- xx
the test data matrix
- yy
the test response vector
- Definition Classes
- PredictorMat → Predictor
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def
eval(): Unit
Compute the error and useful diagnostics for the entire dataset.
Compute the error and useful diagnostics for the entire dataset.
- Definition Classes
- PredictorMat → Predictor
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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
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- PredictorMat
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final
def
getClass(): Class[_]
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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
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val
k: Int
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- PredictorMat
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val
m: Int
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- 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
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final
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notify(): Unit
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final
def
notifyAll(): Unit
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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): VectoD
Given a data matrix 'z', predict the value by summing the predict for each tree, for each row of the matrix.
Given a data matrix 'z', predict the value by summing the predict for each tree, for each row of the matrix.
- z
the data matrix to predict
- Definition Classes
- RegressionTree_GB → PredictorMat
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def
predict(z: VectoD): Double
Given a data vector 'z', predict the value by summing the predict for each tree.
Given a data vector 'z', predict the value by summing the predict for each tree.
- z
the data vector to predict
- Definition Classes
- RegressionTree_GB → 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
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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
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
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def
toString(): String
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def
train(yy: VectoD): RegressionTree_GB
Using Gradient Boosting on Training, for every iteration, we evaluate the residual and form a Regression Tree where the residual is the depedent value(equal to the gradient if using SSE as loss function).
Using Gradient Boosting on Training, for every iteration, we evaluate the residual and form a Regression Tree where the residual is the depedent value(equal to the gradient if using SSE as loss function).
- yy
only the vector in yy will be used in training
- Definition Classes
- RegressionTree_GB → 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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val
x: MatriD
- Attributes
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- Definition Classes
- PredictorMat
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val
y: VectoD
- Attributes
- protected
- Definition Classes
- PredictorMat