class NullModel extends Fit with Predictor with NoFeatureSelection
The NullModel class implements the simplest type of predictive modeling technique
that just predicts the response 'y' to be the mean.
Fit the parameter vector 'b' in the null regression equation
y = b dot x + e = b0 + e
where 'e' represents the residual/error vector (the part not explained by the model).
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Instance Constructors
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new
NullModel(y: VectoD)
- y
the response/output vector
Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
analyze(x_: MatriD = null, y_: VectoD = y, x_e: MatriD = null, y_e: VectoD = y): NullModel
Analyze a dataset using this model using ordinary training with the 'train' method.
Analyze a dataset using this model using ordinary training with the 'train' method.
- x_
the data/input matrix (ignored by
NullModel)- y_
the response/output vector (training/full)
- x_e
the data/input matrix (ignored by
NullModel)- y_e
the response/output vector (testing/full)
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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- protected[lang]
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
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def
corrMatrix(xx: MatriD): MatriD
Return the correlation matrix for the columns in data matrix 'xx'.
Return the correlation matrix for the columns in data matrix 'xx'.
- xx
the data matrix shose correlation matrix is sought
- Definition Classes
- Predictor
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def
diagnose(e: VectoD, yy: VectoD, yp: VectoD, w: VectoD = null, ym_: Double = noDouble): Unit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.
- e
the m-dimensional error/residual vector (yy - yp)
- yy
the actual response/output vector to use (test/full)
- yp
the predicted response/output vector (test/full)
- w
the weights on the instances (defaults to null)
- ym_
the mean of the actual response/output vector to use (training/full)
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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(x_null: MatriD, y_e: VectoD): NullModel
Compute the error vector 'e' (difference between actual and predicted) and useful diagnostics.
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def
f_(z: Double): String
Format a double value.
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def
fit: VectoD
Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method.
Return the Quality of Fit (QoF) measures corresponding to the labels given above in the 'fitLabel' method. 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). Override to add more quality of fit measures.
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def
fitLabel: Seq[String]
Return the labels for the Quality of Fit (QoF) measures.
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def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinkedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinkedHashMapmakes it ordered).- Definition Classes
- QoF
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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def
forwardSel(cols: Set[Int], index_q: Int): (Int, Predictor)
- Definition Classes
- NoFeatureSelection
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
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- @native() @HotSpotIntrinsicCandidate()
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def
getX: MatriD
Return the 'used' data matrix 'x' (for this model it's null).
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def
getY: VectoD
Return the 'used' response vector 'y'.
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def
hashCode(): Int
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- @native() @HotSpotIntrinsicCandidate()
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def
help: String
Return the help string that describes the Quality of Fit (QoF) measures provided by the
Fitclass. -
def
hparameter: HyperParameter
Return the hyper-parameters (the NullModel has none).
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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def
ll(ms: Double = mse0, s2: Double = sig2e, m2: Int = m): Double
The log-likelihood function times -2.
The log-likelihood function times -2. Override as needed.
- ms
raw Mean Squared Error
- s2
MLE estimate of the population variance of the residuals
- Definition Classes
- Fit
- See also
www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf
www.wiley.com/en-us/Introduction+to+Linear+Regression+Analysis%2C+5th+Edition-p-9780470542811 Section 2.11
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val
modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept
- Definition Classes
- Model
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def
modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)
- Definition Classes
- Model
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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
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
parameter: VectoD
Return the vector of parameter/coefficient values.
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def
predict(z: MatriD = null): VectoD
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.
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def
predict(z: VectoD): Double
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', i.e., '[b0] dot [z0]'.
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def
predict(z: VectoI): Double
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
- z
the vector to use for prediction
- Definition Classes
- Predictor
-
def
report: String
Return a basic report on the trained model.
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def
resetDF(df_update: PairD): 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 (model, error)
- Definition Classes
- Fit
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def
residual: VectoD
Return the vector of residuals/errors.
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var
sig2e: Double
- Attributes
- protected
- Definition Classes
- Fit
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def
summary(b: VectoD, stdErr: VectoD, vf: VectoD, 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
- vf
the Variance Inflation Factors (VIFs)
- show
flag indicating whether to print the summary
- Definition Classes
- Fit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
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def
test(modelName: String, doPlot: Boolean = true): Unit
Test the model on the full dataset (i.e., train and evaluate on full dataset).
Test the model on the full dataset (i.e., train and evaluate on full dataset).
- modelName
the name of the model being tested
- doPlot
whether to plot the actual vs. predicted response
- Definition Classes
- Predictor
-
def
toString(): String
- Definition Classes
- AnyRef → Any
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def
train(x_null: MatriD, y_: VectoD): NullModel
Train the predictor by fitting the parameter vector (b-vector) in the null regression equation.
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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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finalize(): Unit
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