Description

Train a regression model with L1-regularization.

Parameters

Name Description Type Required？ Default Value
optimMethod optimization method String null
lambda punish factor. Double ✓
withIntercept Whether has intercept or not, default is true Boolean true
maxIter Maximum iterations, The default value is 100 Integer 100
epsilon Convergence tolerance for iterative algorithms (>= 0), The default value is 1.0e-06 Double 1.0E-6
featureCols Names of the feature columns used for training in the input table String[] null
labelCol Name of the label column in the input table String ✓
weightCol Name of the column indicating weight String null
vectorCol Name of a vector column String null
standardization Whether standardize training data or not, default is true Boolean true

Script Example

Script

  1. data = np.array([
  2.     [2, 1, 1],
  3.     [3, 2, 1],
  4.     [4, 3, 2],
  5.     [2, 4, 1],
  6.     [2, 2, 1],
  7.     [4, 3, 2],
  8.     [1, 2, 1],
  9.     [5, 3, 3]])
  10. df = pd.DataFrame({"f0": data[:, 0], 
  11.                    "f1": data[:, 1],
  12.                    "label": data[:, 2]})
  14. batchData = dataframeToOperator(df, schemaStr='f0 int, f1 int, label int', op_type='batch')
  15. colnames = ["f0","f1"]
  16. lasso = LassoRegTrainBatchOp().setLambda(0.1).setFeatureCols(colnames).setLabelCol("label")
  17. model = batchData.link(lasso)
  19. predictor = LassoRegPredictBatchOp().setPredictionCol("pred")
  20. predictor.linkFrom(model, batchData).print()

Result

f0 f1 label pred
2 1 1 0.830304
3 2 1 1.377312
4 3 2 1.924320
2 4 1 1.159119
2 2 1 0.939909
4 3 2 1.924320
1 2 1 0.502506
5 3 3 2.361724