Classification in R – partial least squares discriminant in R

Hits: 21

Classification in R – partial least squares discriminant in R

Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using partial least squares discriminant analysis (PLS-DA).

PLS-DA is a technique that combines the ideas of partial least squares (PLS) regression and linear discriminant analysis (LDA). PLS is a technique for finding a linear combination of features that maximizes the covariance between the features and the outcome. LDA is a technique for finding a linear boundary that maximizes the separation between the different classes. PLS-DA uses PLS to find a linear combination of features that maximizes both the covariance between the features and the outcome and the separation between the different classes.

In R, there are several packages available for building PLS-DA models, such as the ‘pls’ and ‘plsdepot’ packages. These packages provide functions for creating and training PLS-DA models, as well as functions for evaluating the performance of the model.

The process of building a PLS-DA model in R typically involves the following steps:

Prepare the data: The first step is to prepare the data for the model. This may involve cleaning the data, splitting it into training and testing sets, and scaling the variables.

Define the model: The next step is to define the structure of the model, including the number of latent variables (components) to be used in the model.

Train the model: The model is trained using the prepared data. The model will find the linear combination of features that maximizes both the covariance between the features and the outcome between the different classes.

Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.

Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data by inputting the values of the independent variables and using the linear combination of features found during the training process to predict the class of the new observation.

PLS-DA is a good method for handling high dimensional datasets and correlated predictors. It also can handle both continuous and categorical variables. However, it may not be as robust as other methods when the dataset is small or the number of predictor variables is high. Additionally, the interpretability of the model is often not straightforward as other methods.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Classification in R – partial least squares discriminant in R.



Classification in R – partial least squares discriminant in R

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!

https://setscholars.net/linear-regression-in-r-using-partial-least-squared-regression/

Linear Regression in R – partial least squares regression in R

Classification in R – linear discriminant analysis in R