hanaml.ExponentialRegression {hana.ml.r} | R Documentation |
hanaml.ExponentialRegression is a R wrapper for PAL Exponential Regression algorithm.
hanaml.ExponentialRegression (conn.context, data = NULL, key = NULL, features = NULL, label = NULL, formula = NULL, decomposition = NULL, adjusted.r2 = NULL, pmml.export = NULL)
conn.context |
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data |
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key |
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features |
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label |
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formula |
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decomposition |
Defaults to "LU". |
adjusted.r2 |
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pmml.export |
Defaults to "no". |
R6Class
object.
Exponential regression is an approach to modeling the relationship between a scalar variable y and one or more variables denoted X. In exponential regression, data is modeled using exponential functions, and unknown model parameters are estimated from the data. Such models are called exponential models.
Return a "ExponentialRegression" object with following values:
coefficients: DataFrame
Fitted regression coefficients.
pmml: DataFrame
Regression model content in PMML format.
Set to None if no PMML model was requested.
model: DataFrame
Model is used to save coefficients or PMML model.
If PMML model is requested,
model defaults to PMML model. Otherwise, it is coefficients.
fitted: DataFrame
Predicted dependent variable values for training data.
Set to None if the training data has no row IDs.
statistics: DataFrame
Regression-related statistics, like mean square error, F-statistics, etc.
## Not run: Training DataFrame data: > data ID Y X1 X2 1 0 0.50 0.13 0.33 2 1 0.15 0.14 0.34 3 2 0.25 0.15 0.36 4 3 0.35 0.16 0.35 5 4 0.45 0.17 0.37 6 5 0.55 0.18 0.38 7 6 0.65 0.19 0.39 8 7 0.75 0.19 0.31 9 8 0.85 0.11 0.32 10 9 0.95 0.12 0.33 Train the model: er <- hanaml.ExponentialRegression(conn.context = conn, data = data.fit, key = 'ID', label = 'Y', features = list('X1','X2'), pmml.export='multi-row') Output: > er$coefficients VARIABLE_NAME COEFFICIENT_VALUE 1 __PAL_INTERCEPT__ 2.727731 2 X1 2.674141 3 X2 -6.180427 ## End(Not run)