I have used the KNN algorithm in projects in the past. Here is a summary of when to use.
The K-nearest neighbors (KNN) algorithm is a versatile and simple classification and regression algorithm that can be used in various scenarios. Here are a few situations where the KNN algorithm can be applied:
Classification problems: KNN is commonly used for classification tasks, especially when the decision boundary is nonlinear or complex. It can be used for both binary and multiclass classification problems.
Small to medium-sized datasets: KNN is suitable for datasets with a relatively small number of samples. Since KNN is a lazy learning algorithm, it does not require an explicit training phase and stores the entire dataset in memory. As the dataset size grows, the algorithm becomes less efficient.
Numerical and categorical data: KNN can handle both numerical and categorical feature attributes. However, appropriate feature scaling is essential to prevent features with larger magnitudes from dominating the distance calculations.
Imbalanced datasets: KNN can handle imbalanced datasets without requiring any special modifications. Since KNN considers the neighbors' labels, it can make accurate predictions even when the classes are imbalanced.
Nonlinear decision boundaries: KNN is effective for problems where the decision boundaries are nonlinear. By considering the nearest neighbors, KNN can capture complex relationships between features and target variables.
Recommendation systems: KNN can be used for building recommendation systems based on user-item similarities. By considering the neighbors' preferences or item attributes, KNN can recommend items to users.
It's important to note that the choice of algorithm depends on the specific problem and the characteristics of the dataset. While KNN has its strengths, it may not perform optimally in certain situations, such as datasets with a large number of features or high-dimensional spaces where the curse of dimensionality can affect its performance.
Suppose we have a dataset of flowers with two features: sepal length and sepal width. The target variable is the species of the flower, which can be either "setosa" or "versicolor."
Here's a small subset of the dataset:
Flower Sepal Length Sepal Width Species
Flower 1 5.1 3.5 setosa
Flower 2 4.9 3.0 setosa
Flower 3 6.7 3.1 versicolor
Flower 4 6.0 3.0 versicolor
Suppose we want to classify a new flower with a sepal length of 5.5 and a sepal width of 2.8. We can use KNN to classify this new flower by following these steps:
Choose the value of K: Start by selecting a value for K, which represents the number of nearest neighbors to consider.
Calculate distances: Compute the distance between the new flower and all the other flowers in the dataset. Common distance metrics used in KNN include Euclidean distance, Manhattan distance, or Minkowski distance.
Select the K nearest neighbors: Identify the K flowers with the shortest distances to the new flower.
Count the class labels: Count the number of flowers in each class among the K nearest neighbors.
Determine the class: Assign the class label of the new flower based on the majority class among the K nearest neighbors. In case of a tie, you can use different strategies, such as selecting the class with the highest confidence or taking the average of the predicted class probabilities.
In our example, let's assume we choose K=3. Calculating the distances, we find:
Flower Sepal Length Sepal Width Distance to New Flower
Flower 1 5.1 3.5 0.806
Flower 2 4.9 3.0 1.244
Flower 3 6.7 3.1 1.932
Flower 4 6.0 3.0 1.667
The three nearest neighbors to the new flower are Flower 1, Flower 2, and Flower 4, all of which are "setosa" flowers. Therefore, based on the majority class among the K nearest neighbors, we would classify the new flower as "setosa."
This is a basic example of how the KNN algorithm can classify new data points based on their proximity to existing labeled data points in a dataset.