Classification in R – KNN in R

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Classification in R – KNN 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 k-nearest neighbors (KNN) algorithm.

KNN is a simple and intuitive algorithm that works by finding the k-number of closest observations (neighbors) in the training dataset and using their class labels to predict the class label of a new observation. The distance between the new observation and each of the training observations is calculated using a similarity metric such as Euclidean distance. The observation with the majority class label among its k-nearest neighbors is then assigned as the class label of the new observation.

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:

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 nearest neighbors (k) and the similarity metric.

Train the model: The model is trained using the prepared data. The model will store the training data and their corresponding class labels.

Make predictions: Once the model is trained, it can be used to make predictions on new data by finding the k-nearest neighbors of the new observation in the training dataset and using their class labels to predict the class label of the new observation.

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

KNN algorithm 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, it can be computationally expensive when dealing with large datasets as it needs to calculate the distance between each observation and the new point. The choice of k value, distance metric and scales of the features can greatly impact the performance of the algorithm.

 

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 – KNN in R.

Classification in R – KNN in R

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