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

Evaluation Metrics


In this section, we will go through all the evaluation measures for assessing the quality of the machine learning model predictions. Based on the dependent variable, we have several choices for the evaluation measures. In the train and evaluate step of our Machine Learning Workflow, we mentioned that until we get the desired results, we keep iterating the training model by adding new variables or changing the parameters. In each iteration, we try to optimize for any one or two evaluation metrics. The following table summarizes the various types of metrics used for regression, classification, and recommender systems. Given the scope of this book, we will delve into more details on regression and classification algorithms:

Figure 3.14: Metrics for various types of machine learning algorithms.

Mean Absolute Error (MAE)

Absolute error is direction-agnostic, which means that it is does not matter whether the predicted value of the dependent variable by the model on the test dataset...