Univariate analysis is the study of a single feature/variable. Here, we describe the data to help us get an overall view of how it is organized. For numeric features, such as age, duration, nr.employed (numeric features in the dataset) and many others, we look at summary statistics such as min, max, mean, standard deviation, and percentile distribution. These measures together help us understand the spread of the data. Similarly, for categorical features such as job, marital, and education, we need to study the distinct values in the feature and the frequency of these values. To accomplish this, we can implement a few analytical, visual, and statistical techniques. Let's take a look at the analytical and visual techniques for exploring numeric features.
Applied Supervised Learning with R
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Applied Supervised Learning with R
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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.
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Current Title:
Applied Supervised Learning with R
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface
Free Chapter
R for Advanced Analytics
Exploratory Analysis of Data
Introduction to Supervised Learning
Regression
Classification
Feature Selection and Dimensionality Reduction
Model Improvements
Model Deployment
Capstone Project - Based on Research Papers
Appendix
Customer Reviews