Applied Supervised Learning with R

Price: $48.99
(as of Dec 24,2024 02:18:21 UTC – Details)

Publisher ‏ : ‎ Packt Publishing (May 31, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 502 pages
ISBN-10 ‏ : ‎ 1838556338
ISBN-13 ‏ : ‎ 978-1838556334
Item Weight ‏ : ‎ 1.91 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 1.04 inches

In this post, we will discuss the concept of applied supervised learning with R, a popular programming language for data analysis and machine learning. Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, meaning that the input data is paired with the correct output labels.

R is a powerful tool for implementing supervised learning algorithms, as it has a wide range of packages and libraries specifically designed for machine learning tasks. Some popular packages for supervised learning in R include caret, e1071, and randomForest.

To apply supervised learning with R, you first need to prepare your dataset by splitting it into a training set and a test set. The training set is used to train the model, while the test set is used to evaluate the model’s performance.

Next, you can choose a supervised learning algorithm to train your model. Some common algorithms for supervised learning include linear regression, logistic regression, decision trees, support vector machines, and random forests.

Once you have trained your model, you can evaluate its performance using metrics such as accuracy, precision, recall, and F1 score. You can also visualize the results using plots such as ROC curves and confusion matrices.

Overall, applied supervised learning with R is a powerful technique for building predictive models from labeled data. By using R’s extensive libraries and packages, you can quickly and easily implement supervised learning algorithms and analyze their performance.
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