RegressionModel
Related Docs:
object RegressionModel
| package reg
An identifier for the model. User in score and error reporting.
feature names (parallel to featureFunctions)
feature extracting functions.
representation of the regression model parameters.
a function applied to the inner product of the input vector and weight vector.
an optional calibration spline to Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, Zadrozny, Elkan (ICML, 2001). This is applied prior to invLinkFunction
if provided, we check whether the threshold is exceeded. If so, return an error instead of the computed score. This is for missing data situations.
Container for information returned by RegressionFeatures.constructFeatures.
Container for information returned by RegressionFeatures.constructFeatures. Note that as is, this declaration will cause a compiler warning:
"The outer reference in this type test cannot be checked at run time."
This is a known issue and is a scala bug. See: - https://issues.scala-lang.org/browse/SI-4440 - http://stackoverflow.com/questions/16450008/typesafe-swing-events-the-outer-reference-in-this-type-test-cannot-be-checked-a
A solution that would remove the warning is to make the class not final. Not doing this just to remove a warning.
features that were extracted from an input value.
map from feature name to variables in the feature function that were missing.
whether the number of
representation of the regression model parameters.
Extract the features from the raw data by mapping featureFunctions over the input.
Extract the features from the raw data by mapping featureFunctions over the input. If numMissingThreshold is not None and the number of resulting empty Iterables exceeds the numMissingThreshold value, then the resulting Features.missingOk value is false; otherwise, it will be true. If Features.missingOk is false, then go back and check all feature functions for missing values and add findings to the Features.missing map. This Features.missing is a mapping from the feature specification to the list of variable names whose associated values are missing from the input.
raw input data of the model input type.
a Features instance with the following: 1 the transformed input vector 1 the map of bad features to the missing values in the raw data that were needed to compute the feature 1 whether the amount of missing data is acceptable to still continue
Issue a debug logging message, with an exception.
Issue a debug logging message, with an exception.
the message object. toString() is called to convert it
to a loggable string.
the exception to include with the logged message.
Issue a debug logging message.
Issue a debug logging message.
the message object. toString() is called to convert it
to a loggable string.
Issue a error logging message, with an exception.
Issue a error logging message, with an exception.
the message object. toString() is called to convert it
to a loggable string.
the exception to include with the logged message.
Issue a error logging message.
Issue a error logging message.
the message object. toString() is called to convert it
to a loggable string.
feature extracting functions.
feature extracting functions.
feature names (parallel to featureFunctions)
feature names (parallel to featureFunctions)
Issue a info logging message, with an exception.
Issue a info logging message, with an exception.
the message object. toString() is called to convert it
to a loggable string.
the exception to include with the logged message.
Issue a info logging message.
Issue a info logging message.
the message object. toString() is called to convert it
to a loggable string.
a function applied to the inner product of the input vector and weight vector.
Determine whether debug logging is enabled.
Determine whether debug logging is enabled.
Determine whether error logging is enabled.
Determine whether error logging is enabled.
Determine whether info logging is enabled.
Determine whether info logging is enabled.
Determine whether trace logging is enabled.
Determine whether trace logging is enabled.
Determine whether warn logging is enabled.
Determine whether warn logging is enabled.
The logger is a @transient lazy val to enable proper working with Spark.
The logger is a @transient lazy val to enable proper working with Spark.
The logger will not be serialized with the rest of the class with which this
trait is mixed-in.
The name with which the logger is initialized.
The name with which the logger is initialized. This can be overridden in a derived class.
Get the name associated with this logger.
Get the name associated with this logger.
the name.
An identifier for the model.
An identifier for the model. User in score and error reporting.
if provided, we check whether the threshold is exceeded.
if provided, we check whether the threshold is exceeded. If so, return an error instead of the computed score. This is for missing data situations.
an optional calibration spline to Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, Zadrozny, Elkan (ICML, 2001).
an optional calibration spline to Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, Zadrozny, Elkan (ICML, 2001). This is applied prior to invLinkFunction
Get the score.
Issue a trace logging message, with an exception.
Issue a trace logging message, with an exception.
the message object. toString() is called to convert it
to a loggable string.
the exception to include with the logged message.
Issue a trace logging message.
Issue a trace logging message.
the message object. toString() is called to convert it
to a loggable string.
Issue a warn logging message, with an exception.
Issue a warn logging message, with an exception.
the message object. toString() is called to convert it
to a loggable string.
the exception to include with the logged message.
Issue a warn logging message.
Issue a warn logging message.
the message object. toString() is called to convert it
to a loggable string.
A regression model capable of doing not only linear regression but polynomial regression in general.
This is useful because these conversions allow implicit conversion function from some of the AnyVal types and Options of AnyVal types to Iterable[(String, Double)]. This is useful because specifying features in the JSON spec like:
{ ... "features": { "intercept": "-3", "income": "${user.profile.income}" } }into sequences like:
For more information, see com.eharmony.aloha.models.reg.RegressionModelValueToTupleConversions.
model input type
model output type. to convert from B to com.eharmony.aloha.score.Scores.Score
An identifier for the model. User in score and error reporting.
feature names (parallel to featureFunctions)
feature extracting functions.
representation of the regression model parameters.
a function applied to the inner product of the input vector and weight vector.
an optional calibration spline to Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, Zadrozny, Elkan (ICML, 2001). This is applied prior to invLinkFunction
if provided, we check whether the threshold is exceeded. If so, return an error instead of the computed score. This is for missing data situations.