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PySpark Cookbook

You're reading from  PySpark Cookbook

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Pages 330 pages
Edition 1st Edition
Languages
Authors (2):
Denny Lee Denny Lee
Profile icon Denny Lee
Tomasz Drabas Tomasz Drabas
Profile icon Tomasz Drabas
View More author details
Toc

Table of Contents (13) Chapters close

Title Page
Packt Upsell
Contributors
Preface
1. Installing and Configuring Spark 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark Index

Introducing Estimators


The Estimator class, just like the Transformer class, was introduced in Spark 1.3. The Estimators, as the name suggests, estimate the parameters of a model or, in other words, fit the models to data.

In this recipe, we will introduce two models: the linear SVM acting as a classification model, and a linear regression model predicting the forest elevation.

Here is a list of all of the Estimators, or machine learning models, available in the ML module:

  • Classification:
    • LinearSVC is an SVM model for linearly separable problems. The SVM's kernel has the 

       form (a hyperplane), where 

       is the coefficients (or a normal vector to the hyperplane), 

       is the records, and b is the offset.

    • LogisticRegressionis a default, go-to classification model for linearly separable problems. It uses a logit function to calculate the probability of a record being a member of a particular class.

    • DecisionTreeClassifier is a decision tree-based model used for classification purposes. It builds a binary...
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