Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface

Poisson Regression


In linear regression, we saw an equation of the form:

In Poisson Regression, the response variable Y is a count or rate (Y/t) that has a Poisson distribution with expected (mean) count of as , which is equal to variance.

In case of logistic regression, we would probe for values that can maximize log-likelihood to get the maximum likelihood estimators (MLEs) for coefficients.

There are no closed-form solutions, hence the estimations of maximum likelihood would be obtained using iterative algorithms such as Newton-Raphson and Iteratively re-weighted least squares (IRWLS).

Poisson regression is suitable for the count-dependent variable, which must meet the following guidelines:

  • It follows a Poisson distribution

  • Counts are not negative

  • Values are whole numbers (no fractions)

Note

The dataset used here to demonstrate Poisson regression comes from A. Colin Cameron and Per Johansson, "Count Data Regression Using Series Expansion: With Applications", Journal of Applied Econometrics, Vol...