K-Nearest Neighbors (KNN) is a popular and simple algorithm used in machine learning for both classification and regression tasks. It is a non-parametric and instance-based learning method that relies on the principle of similarity. In KNN, the “k” represents the number of nearest neighbours that are considered to make predictions for new, unseen data points.
The working principle of KNN involves finding the “k” closest data points in the training dataset to the input query point. These nearest neighbours are determined using a distance metric, commonly the Euclidean distance in feature space. The feature space consists of the input variables or attributes that describe each data point. Once the “k” nearest neighbours are identified, the algorithm then utilizes their known target values to predict the label or value for the new data point.
For classification tasks, KNN assigns the most frequent class among the “k” neighbors to the new data point. In contrast, for regression tasks, KNN calculates the average (or another suitable aggregation) of the target values from the “k” neighbors to predict the continuous output for the new data point. Apart from it by obtaining a Machine Learning Certification, you can advance your career in Machine Learning. With this course, you can demonstrate your expertise in designing and implementing a model building, creating AI and machine learning solutions, performing feature engineering, many more fundamental concepts, and many more critical concepts among others.
One of the essential considerations in KNN is the selection of an appropriate value for “k.” A smaller value of “k” might lead to noisy predictions, making the model sensitive to outliers in the data. On the other hand, a larger “k” value can result in overly smooth decision boundaries, potentially overlooking local patterns in the data.
KNN is an instance-based method because it stores the entire training dataset and makes predictions directly from the stored data without explicitly learning a model. This aspect makes KNN computationally expensive, particularly for large datasets, as it requires calculating the distance between the query point and all training samples during prediction.
While KNN is relatively straightforward and can be effective in certain scenarios, it has its limitations. It may struggle with high-dimensional data or when the feature space is sparse, as the concept of distance becomes less meaningful. Additionally, KNN’s performance can be impacted by imbalanced data, as the majority class might dominate the predictions in such cases.
Despite its limitations, KNN serves as an essential baseline algorithm in many machine learning tasks, and variations of the method have been developed to address specific challenges. It is widely used in various fields and remains a valuable tool in the arsenal of machine learning techniques.
