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

1 vote133 views1 answer
How to import PyMem python
Python
0 vote107 views1 answer
No module named cbor2 windows
Python
0 vote117 views1 answer
Python set list index value that doesn't exist
Python
0 vote112 views1 answer
Ordenar lista python
Python
0 vote89 views1 answer
HTTP Error 403: Forbidden django account signup email
Python Django
2 votes135 views3 answers
Scatter plot actual vs predicted python
Python
-2 votes107 views2 answers
How to find eigenvalues in python
Python
4 votes127 views2 answers
Queue python
Python
0 vote116 views1 answer
Adding attributes and metadata to a dataset using xarray
Python
0 vote107 views1 answer
How to wirte something 100 times with python
Python