Description
The random forest use the bagging to prevent the overfitting.
In the operator, we implement three type of decision tree to increase diversity of the forest.
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id3
cart
c4.5
-
information
gini
information ratio
mse
Parameters
Name | Description | Type | Required? | Default Value |
---|---|---|---|---|
predictionCol | Column name of prediction. | String | ✓ | |
predictionDetailCol | Column name of prediction result, it will include detailed info. | String | ||
reservedCols | Names of the columns to be retained in the output table | String[] | null |
Script Example
Code
import numpy as np
import pandas as pd
from pyalink.alink import *
def exampleData():
return np.array([
[1.0, "A", 0, 0, 0],
[2.0, "B", 1, 1, 0],
[3.0, "C", 2, 2, 1],
[4.0, "D", 3, 3, 1]
])
def sourceFrame():
data = exampleData()
return pd.DataFrame({
"f0": data[:, 0],
"f1": data[:, 1],
"f2": data[:, 2],
"f3": data[:, 3],
"label": data[:, 4]
})
def batchSource():
return dataframeToOperator(
sourceFrame(),
schemaStr='''
f0 double,
f1 string,
f2 int,
f3 int,
label int
''',
op_type='batch'
)
def streamSource():
return dataframeToOperator(
sourceFrame(),
schemaStr='''
f0 double,
f1 string,
f2 int,
f3 int,
label int
''',
op_type='stream'
)
trainOp = (
DecisionTreeRegTrainBatchOp()
.setLabelCol('label')
.setFeatureCols(['f0', 'f1', 'f2', 'f3'])
)
predictBatchOp = (
DecisionTreeRegPredictBatchOp()
.setPredictionCol('pred')
)
(
predictBatchOp
.linkFrom(
batchSource().link(trainOp),
batchSource()
)
.print()
)
predictStreamOp = (
DecisionTreeRegPredictStreamOp(
batchSource().link(trainOp)
)
.setPredictionCol('pred')
)
(
predictStreamOp
.linkFrom(
streamSource()
)
.print()
)
StreamOperator.execute()
Result
f0 f1 f2 f3 label pred
0 1.0 A 0 0 0 0.0
1 2.0 B 1 1 0 0.0
2 3.0 C 2 2 1 1.0
3 4.0 D 3 3 1 1.0