Machine Learning with KNN in R: A Step-by-Step Guide

In this article, we will explore how to use the K Nearest Neighbors (KNN) algorithm for classification tasks in R using the class package. We will go through the process of preparing the data, understanding the KNN algorithm, and implementing it using the knn() function from the class package.

Understanding KNN

KNN is a supervised learning algorithm that predicts the target value for a new instance by finding the k most similar instances in the training dataset. The similarity between instances is measured using a distance metric such as Euclidean distance or Manhattan distance. The prediction is based on the majority vote of the k nearest neighbors.

The KNN algorithm has several advantages, including:

Handling non-linear relationships between variables
Robust to outliers and noisy data
Easy to implement

However, KNN also has some limitations, such as:

Computationally expensive for large datasets
Requires careful selection of the value of k (neighbor count)

Preparation of Data

Before applying the KNN algorithm, we need to prepare our dataset. This includes:

Handling missing values: If there are missing values in our dataset, we will need to decide how to handle them. In this example, we assume that the data does not have any missing values.
Scaling variables: Some variables may have different scales than others. We will use standardization to scale all variables to a common range.

The `class` Package

The class package in R provides functions for classification tasks, including KNN. The knn() function takes the following arguments:

train: The training data
test: The testing data
cl: The class vector (i.e., the target values)

However, there is a catch! In this example, we are getting an error because of incorrect argument lengths.

Correcting Argument Lengths

The error occurs because the length of train and class do not match. This can be fixed by using one of the following solutions:

Reshape the class vector to have the same number of rows as the training data
Use a different function that does not require the cl argument

Reshaping the Class Vector

We can reshape the class vector to match the length of the training data by using the following code:

# Create a new column for the class vector
accidents_ml$class <- accidents_ml$class

# Drop the original class column
accidents_ml <- accidents_ml[, -3]

# Reshape the class vector
train_class <- accidents_ml$class[train_rows]

Using the `knn()` Function with Corrected Argument Lengths

With the corrected argument lengths, we can apply the KNN algorithm using the following code:

# Create a new column for the class vector
accidents_ml$class <- accidents_ml$class

# Drop the original class column
accidents_ml <- accidents_ml[, -3]

# Reshape the class vector
train_class <- accidents_ml$class[train_rows]

# Apply the KNN algorithm
knn_output <- knn(train = train_data, test = test_data, cl = train_class)

Interpreting the Output

The output of the knn() function is a vector containing the predicted class labels for each instance in the testing data.

Handling Non-Numeric Variables

In the example above, we assumed that all variables are numeric. However, if there are non-numeric variables in our dataset, they need to be scaled or standardized before applying KNN.

We can use the scale() function from the stats package to standardize our variables:

# Standardize the data
train_data_scaled <- scale(train_data)

Choosing the Value of k

The value of k is a hyperparameter that needs to be chosen. The optimal value of k depends on the specific dataset and problem.

One common approach is to use cross-validation to find the optimal value of k.

# Create a new column for the class vector
accidents_ml$class <- accidents_ml$class

# Drop the original class column
accidents_ml <- accidents_ml[, -3]

# Reshape the class vector
train_class <- accidents_ml$class[train_rows]

# Apply cross-validation to find the optimal value of k
optimal_k <- optim(k = 1:10, expression = {
    sum(knn(train = train_data_scaled, test = test_data, cl = train_class)^2)
}, method = "L-BFGS-B")

Conclusion

In this article, we explored how to use the K Nearest Neighbors (KNN) algorithm for classification tasks in R using the class package. We covered the preparation of data, understanding the KNN algorithm, and implementing it using the knn() function from the class package.

We also discussed common issues such as handling non-numeric variables and choosing the value of k.

With these insights, you can now apply KNN to your own classification problems in R.

Last modified on 2024-02-27