Non-Linear Regression in R – KNN in R

Non-linear regression is a type of statistical analysis that is used to model relationships between variables that are not linear. In other words, it is used to model relationships where the change in one variable is not directly proportional to the change in another variable. One popular method of non-linear regression is using k-nearest neighbors (KNN) algorithm.

KNN is a type of supervised machine learning algorithm that can be used for both classification and regression tasks. It works by finding the k number of nearest points to a new data point and uses the average of their value to predict the value for the new point.

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

The process of building a KNN model in R typically involves the following steps:

1. 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.
2. Define the model: The next step is to define the structure of the model, including the number of nearest neighbors (k) to be considered.
3. Train the model: The model is trained using the prepared data. The model will use the k nearest points to predict the value for new point
4. Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.
5. Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data.

By using KNN in R, you can model non-linear relationship and get accurate predictions. It’s simple, easy to understand and fast for small data sets. However, it’s not recommended for large data sets as the computation time increases exponentially with the size of the data set.

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: Non-Linear Regression in R – KNN in R.

Non-Linear Regression in R – KNN in R

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