In data analysis, it is common to try to explain the relationship between a target variable and one or more explanatory variables. Parametric methods such as linear regression, logistic regression, or certain neural networks are based on the idea that this relationship can be expressed through a set of parameters. These parameters, estimated during training, make it possible to directly measure the influence of each variable and keep the model interpretable.

‍
However, not all situations fit this framework. There are contexts where no simple equation can summarize the relationship between variables. In such cases, non-parametric methods are used. Instead of imposing a predefined form, these techniques rely on the structure of the data itself. Among them are decision trees, the naïve Bayes classifier, and K-Nearest Neighbors (KNN).

‍
KNN perfectly illustrates this logic: it does not try to estimate coefficients to explain a global trend but instead reasons by proximity. To assign a value or a category to a new record, it looks at the most similar known examples and deduces the answer based on these neighbors.

How the K-Nearest Neighbors (KNN) method works ?

KNN is a non-parametric method that relies solely on the concept of similarity between records. The idea is simple: when a value is missing, or when a new record must be classified, the algorithm searches the dataset for the k closest records. Proximity is measured using a distance metric calculated from the available variables. The value to predict is then inferred from these neighbors.

‍
Let’s take a concrete example in the automotive sector. Imagine a dataset containing cars with their characteristics (price, age, horsepower, weight, mileage, fuel type). If a car is missing information about its mileage, KNN will identify the most similar vehicles (based on price, age, horsepower, etc.) and estimate the mileage by averaging that of its neighbors.

‍
The same principle applies to classification. Suppose we want to determine whether a car runs on diesel or gasoline. KNN compares this car to its closest known neighbors and assigns the dominant fuel type among them. In all cases, the reasoning relies on the idea that “similar objects tend to share the same characteristics.” The more relevant the number of neighbors considered, the more robust the prediction.

KNN offers several advantages for businesses. It is simple to implement, requiring no complex modeling. It is also highly flexible, as it can be applied to both classification and estimation problems. Moreover, its logic of proximity is easily interpretable.

‍
However, KNN also comes with limitations that organizations must keep in mind. Its performance depends heavily on the quality and volume of data. In very large datasets, distance calculations can become costly in terms of time and resources. It is also sensitive to the choice and scale of variables: without proper preparation (normalization, cleaning), the model can produce biased results.