Classification in R – linear discriminant analysis in R

Classification in R – linear discriminant analysis 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 linear discriminant analysis (LDA).

LDA is a technique for finding a linear combination of features that separates different classes or categories. It works by finding a linear boundary that maximizes the separation between the different classes. This boundary is called a discriminant, and the linear combination of features that defines it is called a linear discriminant.

In R, there are several packages available for building LDA models, such as the ‘MASS’ and ‘lda’ packages. These packages provide functions for creating and training LDA models, as well as functions for evaluating the performance of the model.

The process of building a LDA 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 linear discriminants and the regularization parameter.

Train the model: The model is trained using the prepared data. The model will find the linear combination of features that maximizes the separation 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.

LDA is simple to understand and easy to implement, it’s also good at dealing with high-dimensional data and can handle categorical variables as well. However, LDA assumes that the data is normally distributed and that the variances of the predictors are equal for all classes. It is also sensitive to the presence of outliers. With LDA, the interpretability of the model is often more straightforward than other more complex models.

 

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 – linear discriminant analysis in R.

Classification in R – linear discriminant analysis in R

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