Introduction to Logistic Regression in PySpark | by Gustavo Santos | Nov, 2023

Tutorial to run your first classification mannequin in Databricks

Picture by Ibrahim Rifath on Unsplash

Massive Knowledge. Giant datasets. Cloud…

These phrases are in all places, following us round and within the ideas of shoppers, interviewers, managers and administrators. As information will get increasingly more considerable, datasets solely improve in dimension in a fashion that, typically, it’s not potential to run a machine studying mannequin in an area atmosphere — in a single machine, in different phrases.

This matter requires us to adapt and discover different options, equivalent to modeling with Spark, which is likely one of the most used applied sciences for Massive Knowledge. Spark accepts languages equivalent to SQL, Python, Scala, R and it has its personal strategies and attributes, together with its personal Machine Studying library [MLlib]. While you work with Python in Spark, it’s referred to as PySpark, for instance.

Moreover, there’s a platform referred to as Databricks that wraps Spark in a really nicely created layer that permits information scientists to work on it identical to Anaconda.

After we’re making a ML mannequin in Databricks, it additionally accepts Scikit Study fashions, however since we’re extra occupied with Massive Knowledge, this tutorial is all created utilizing Spark’s MLlib, which is extra fitted to massive datasets and in addition this fashion we add a brand new software to our talent set.

Let’s go.

The dataset for this train is already inside Databricks. It’s one of many UCI datasets, Adults, that’s an extract from a Census and labeled with people that earn much less or greater than $50k per yr. The information is publicly obtainable on this deal with:

Our tutorial is to construct a binary classifier that tells whether or not an individual makes much less or greater than $50k of earnings in a yr.

On this part, let’s go over every step of our mannequin.

Listed below are the modules we have to import.

from pyspark.sql.features import col
from import UnivariateFeatureSelector
from import RFormula
from import StringIndexer, VectorAssembler
from import…

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