multinomialNB
Syntax
multinomialNB(Y, X, [varSmoothing=1.0])
Arguments
Y is a vector with the same length as table X. Each element of labels indicates the class that the correponding row in X belongs to.
X is a table indicating the training set. Each row is a sample and each column is a feature.
varSmoothing is a positive floating number between 0 and 1 indicating the additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
Details
-
model: a RESOURCE data type variable. It is an internal binary resource generated by function
multinomialNB
and to be used by function predict. -
modelName: string "multinomialNB".
-
varSmoothing: varSmoothing parameter value.
Examples
The dataset iris.data used in the following example can be downloaded from https://archive.ics.uci.edu/ml/datasets/iris.
DATA_DIR = "C:/DolphinDB/Data"
t = loadText(DATA_DIR+"/iris.data")
t.rename!(`col0`col1`col2`col3`col4, `sepalLength`sepalWidth`petalLength`petalWidth`class)
t[`classType] = take(0, t.size())
update t set classType = 1 where class = "Iris-versicolor"
update t set classType = 2 where class = "Iris-virginica"
training = select sepalLength, sepalWidth, petalLength, petalWidth from t
labels = t.classType
model = multinomialNB(labels, training);
predict(model, training);