s_NBayes | R Documentation |
Train a Naive Bayes Classifier using e1071::naiveBayes
s_NBayes(
x,
y = NULL,
x.test = NULL,
y.test = NULL,
laplace = 0,
x.name = NULL,
y.name = NULL,
print.plot = FALSE,
plot.fitted = NULL,
plot.predicted = NULL,
plot.theme = rtTheme,
question = NULL,
verbose = TRUE,
outdir = NULL,
save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
...
)
x |
Numeric vector or matrix / data frame of features i.e. independent variables |
y |
Numeric vector of outcome, i.e. dependent variable |
x.test |
Numeric vector or matrix / data frame of testing set features
Columns must correspond to columns in |
y.test |
Numeric vector of testing set outcome |
laplace |
Float (>0): Laplace smoothing. Default = 0 (no smoothing). This only affects categorical features |
x.name |
Character: Name for feature set |
y.name |
Character: Name for outcome |
print.plot |
Logical: if TRUE, produce plot using |
plot.fitted |
Logical: if TRUE, plot True (y) vs Fitted |
plot.predicted |
Logical: if TRUE, plot True (y.test) vs Predicted.
Requires |
plot.theme |
Character: "zero", "dark", "box", "darkbox" |
question |
Character: the question you are attempting to answer with this model, in plain language. |
verbose |
Logical: If TRUE, print summary to screen. |
outdir |
Path to output directory.
If defined, will save Predicted vs. True plot, if available,
as well as full model output, if |
save.mod |
Logical: If TRUE, save all output to an RDS file in |
... |
Additional arguments |
The laplace
argument only affects categorical predictors
rtMod
object
E.D. Gennatas
Other Supervised Learning:
s_AdaBoost()
,
s_AddTree()
,
s_BART()
,
s_BRUTO()
,
s_BayesGLM()
,
s_C50()
,
s_CART()
,
s_CTree()
,
s_EVTree()
,
s_GAM()
,
s_GBM()
,
s_GLM()
,
s_GLMNET()
,
s_GLMTree()
,
s_GLS()
,
s_H2ODL()
,
s_H2OGBM()
,
s_H2ORF()
,
s_HAL()
,
s_KNN()
,
s_LDA()
,
s_LM()
,
s_LMTree()
,
s_LightCART()
,
s_LightGBM()
,
s_MARS()
,
s_MLRF()
,
s_NLA()
,
s_NLS()
,
s_NW()
,
s_PPR()
,
s_PolyMARS()
,
s_QDA()
,
s_QRNN()
,
s_RF()
,
s_RFSRC()
,
s_Ranger()
,
s_SDA()
,
s_SGD()
,
s_SPLS()
,
s_SVM()
,
s_TFN()
,
s_XGBoost()
,
s_XRF()
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