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

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