new LogisticRegressionWithLBFGS()
Train a classification model for Multinomial/Binary Logistic Regression using
Limited-memory BFGS. Standard feature scaling and L2 regularization are used by default.
NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1}
for k classes multi-label classification problem.
Methods
run(input, initialWeightsopt) → {module:eclairjs/mllib/classification.LogisticRegressionModel}
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
input |
module:eclairjs.RDD | ||
initialWeights |
module:eclairjs/mllib/linalg.Vector |
<optional> |
Returns:
setNumClasses(numClasses) → {module:eclairjs/mllib/classification.LogisticRegressionWithLBFGS}
Set the number of possible outcomes for k classes classification problem in
Multinomial Logistic Regression.
By default, it is binary logistic regression so k will be set to 2.
Parameters:
| Name | Type | Description |
|---|---|---|
numClasses |
integer |