This chapter focuses on building a Logistic Regression Model with PySpark along with understanding the ideas behind logistic regression. Logistic regression is used for classification problems. We have already seen classification details in earlier chapters. Although it is used for classification, it’s still called logistic regression. This is due to the fact that under the hood, linear regression equations still operate to find the relationship between input variables and the target variables. The main distinction between linear and logistic regression is that we use some ...
© Pramod Singh 2019
Pramod SinghMachine Learning with PySpark https://doi.org/10.1007/978-1-4842-4131-8_55. Logistic Regression
Pramod Singh1
(1)
Bangalore, Karnataka, India
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