VectorIndexer pyspark

from pyspark.ml import Pipeline
from pyspark.ml.regression import LinearRegression
from pyspark.ml.feature import VectorIndexer
from pyspark.ml.evaluation import RegressionEvaluator

# Automatically identify categorical features, and index them.
# We specify maxCategories so features with > 4 distinct values are treated as continuous.

featureIndexer = VectorIndexer(inputCol="features", \
                               outputCol="indexedFeatures",\
                               maxCategories=4).fit(transformed)

data = featureIndexer.transform(transformed)

Source: runawayhorse001.github.io

Similar codes

28 votes103 views5 answers
Python style console output
Python
0 vote117 views1 answer
Python how to change a point in a multidimensional list
Python
-1 vote115 views1 answer
Unocode error pytonn
Python
0 vote120 views1 answer
Python pyowm
Python
1 vote111 views1 answer
Pyspark Aggregation on multiple columns
Python
3 votes112 views1 answer
How does scope work in python
Python
0 vote83 views1 answer
Loop through words in a string python
Python
1 vote115 views1 answer
How to access for loop counter of outer loop
Python
0 vote140 views1 answer
Python join list ignore none and empty string
Python
4 votes114 views2 answers
How to print time python
Python