Machine Learning in R | Classification | Data Science for Beginners | IRIS | LDA | CARET tutorials

Hits: 237

 

 

Machine learning is a method of teaching computers to learn from data without being explicitly programmed. One of the most commonly used algorithms for classification tasks is the Linear Discriminant Analysis (LDA) algorithm. In this article, we will be discussing how to use LDA for classification in R using the IRIS dataset from UCI.

The IRIS dataset is a popular dataset for classification tasks and contains 150 observations of iris flowers, including their sepal length, sepal width, petal length, and petal width. The dataset also contains the species of the iris flower, which can be used as the target variable for classification.

To begin, we will need to load the IRIS dataset into R. This can be done using the built-in iris dataset in R or by importing the data from a file. Once the data is loaded, we will need to split the data into training and testing sets. This is important as we want to use the training set to train our model and the testing set to evaluate its performance.

Next, we will need to install and load the MASS library, which contains the LDA function. Once the library is loaded, we can use the lda() function to train our model. The function requires us to specify the predictor variables, the response variable, and the number of classes. In this case, the predictor variables will be the sepal length, sepal width, petal length, and petal width, and the response variable will be the species of the iris flower.

Once the model is trained, we can use the predict() function to make predictions on new data. The predict() function requires us to provide the model and the new data, and it will return the predicted class for each observation. We can then use the confusionMatrix() function to evaluate the performance of our model by comparing the predicted classes to the true classes.

In conclusion, using LDA for classification in R is a simple and straightforward process. By following these steps, we can easily train and evaluate a model using the IRIS dataset, which can be used for classification tasks. It is important to note that LDA is a linear algorithm and may not perform well on non-linear datasets. In those cases, other algorithms such as Random Forest or Neural Networks may be more appropriate.

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:

Machine Learning in R | Classification | Data Science for Beginners | IRIS | LDA | CARET tutorials.

What should I learn from this Applied Machine Learning & Data Science tutorials?

You will learn:

 

Machine Learning in R | Classification | Data Science for Beginners | IRIS | LDA | CARET tutorials:



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

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!