K-NN Classifiers: The Secret Sauce Behind Robust Case Classification

As discussed in the prequel to this blog post, case classification is paramount for a support ticket to be routed to the best-fit agent right off the bat. However, with a plethora of classification techniques available in machine learning (ML), which one should you pick?

We, at SearchUnify, use K-Nearest Neighbor algorithms (K-NN) because of their simple implementation, robustness against noisy data points, little to no multi-class classification problems, and much more.

Let’s get into the nitty-gritty of the K-NN case classifiers for a better understanding.

What are K-NN Classifiers?

K-NN classifiers are a type of ML algorithm that is used for both classification and regression tasks.

They calculate the likelihood of a new data point joining the group based on the proximity to the data points from the predetermined group. In this classification technique, you determine how to classify the data according to its nearest neighbors.

Additionally, K-NN classifiers are known as lazy learning algorithms. Why, you may ask? Because they do not learn a model from the training data. Instead, they memorize the training data and use it for prediction at test time. This makes the algorithm computationally expensive as it requires large datasets to completer. It is also flexible to non-linear and non-parametric relationships between the features and the target variable.

How Does K-NN Classification Algorithm Work?

Imagine that you have a set of data points that represent different types of fruits based on their weight and texture. You want to use K-NN to classify a new fruit that you have just encountered. To do this:

1. Select a value for K, which represents the number of nearest neighbors that you want to use to classify the new fruit. Let’s say that you choose K=3.
2. Calculate the distance between the new fruit and each of the existing data points in the dataset. In this case, the distance is calculated based on the weight and texture of each fruit.
3. Once you have calculated the distances between the new fruit and all of the existing data points, you would select the K nearest neighbors based on the shortest distances.
4. Finally, determine the majority class among the K nearest neighbors, and classify the new fruit as that class.

This is just a simple example; in practice, K-NN can be used for a variety of classification tasks, such as image classification, text classification, and recommendation systems. Here is a quick visual representation of how K-NN works:

In this illustration, the new fruit is represented by the orange dot, and the existing data points are represented by blue and green dots. The distances between the new fruit and each of the existing data points are calculated, and the K-nearest neighbors (represented by the green dots) are selected. In this case, the majority class among them is “apple”, so the new fruit is classified as an apple.

Still unclear?

Here’s Another Example For a Better Understanding

Suppose we have a new dataset and we want to predict whether a 50-year-old patient with a BMI of 30, blood pressure of 120/80, and glucose level of 150 has diabetes or not, we can leverage the K-NN algorithm.

Here is a quick run-through of the steps to do so:

1. Calculate the distance between the new patient and each of the patients in the training set using a distance metric such as Euclidean distance.
2. Select the k-nearest neighbors of the new patient based on the calculated distances.
3. Finally, assign the label of the new patient based on the most common label among the k-nearest neighbors.

If the majority of the k-nearest neighbors have diabetes, then it can be predicted that the new patient also has diabetes.

The Benefits of K-NN Case Classification

So, now that we are done with the basics, let us gauge the benefits and why you should be deploying K-NN classification technique to assess your datasets:

1. Simple and Comprehensive: K-NN classification is simple to grasp, and its basic idea can be explained in a few sentences.
2. Data Distribution Without Assumptions: K-NN does not make any assumptions about the distribution of the data. It can handle any kind of data, whether it is linearly separable or not.
3. Non-Parametric: K-NN is a non-parametric algorithm, which means that it does not make any assumptions about the underlying data distribution. This makes it more robust than other parametric algorithms, especially when the data is noisy or the distribution is unknown.
4. Works Well with Large Datasets: K-NN is a lazy learning algorithm, which means that it does not require training data to build a model. Instead, it stores all the training data and uses it during testing. This makes K-NN very efficient and effective for large datasets.
5. Functions Well with Imbalanced Data: K-NN can handle imbalanced datasets, where some classes have many more instances than others. This is because K-NN is based on the nearest neighbors, so it can still make accurate predictions for minority classes.

In conclusion, K-NN is a versatile and powerful algorithm that can be used in a wide range of applications. It is particularly useful when the data is non-linear or when there is no clear separation between classes.

Experience K-NN Classifiers Live in Action

Wondering how we leverage K-NN Classifiers to ensure pitch-perfect customer case management? Then request a demo today and watch the magic unfold.

Also, since we are done with Intelligent Classification, let’s hop on to the next stepping stone of our triaging series– Intelligent Case Prioritization.