Is KNN supervised or unsupervised?

The K-Nearest Neighbors (KNN) algorithm is considered a Supervised machine learning algorithm because it requires labeled data to train the model; the algorithm makes predictions based on the closest neighbors from this labeled training data.

Why KNN is assumed to be Unsupervised learning?

K-Nearest Neighbors (KNN) is sometimes viewed as "unsupervised-like" due to its similarities with algorithms like k-means. Here are the key points of comparison:

1. Similarity in the Role of ‘K’:

• K-means: 'K' represents the number of clusters to form.
• KNN: 'K' denotes the number of nearest neighbors considered for predictions.

2. Distance-Based Approach:

Both KNN and k-means rely on distance metrics (e.g., Euclidean distance) to assess relationships between points. They use proximity as a critical factor—k-means for forming clusters and KNN for classifying or regressing based on neighbors.

How KNN Utilizes Labeled Data?

KNN operates directly on the labeled training data. Each instance in the training set contains both the input features and the associated label. These labels are essential for KNN because they are used during the prediction phase to determine the class or value of new instances.

Prediction Process:

1. Distance Measurement: KNN calculates the distance between the new instance and each instance in the training dataset. This distance measurement (often Euclidean) helps in determining which instances are the "nearest neighbors."
2. Neighbor Selection: The algorithm selects the 'k' closest instances (neighbors) to the new instance. The value of 'k' is a predefined parameter that significantly influences the outcome. A smaller 'k' makes the algorithm sensitive to noise in the data, while a larger 'k' may smooth out the prediction too much.
3. Aggregating Neighbor Labels: For classification tasks, KNN predicts the label of the new instance based on the most common label among its nearest neighbors. In regression tasks, it might compute the average or median of the values among the nearest neighbors.

Key Features of KNN

• No Explicit Training Phase: While KNN is a supervised method, it does not build an internal model. It simply stores the training data and does "lazy learning" when a prediction is needed.
• Sensitivity to Neighbors: The choice of 'k' and the distance metric can greatly affect the algorithm's performance. A smaller 'k' can make the model sensitive to noise, while a larger 'k' makes it more computationally intensive.
• Versatility: KNN can be used for both classification (predicting a label) and regression (predicting a continuous value), making it versatile in handling various types of data.