What is KNN?

KNN is one of the most basic yet essential classification algorithms in machine learning. It is heavily used in pattern recognition, data mining, and intrusion detection and is a member of the supervised learning domain.

Since it is non-parametric, which means it does not make any underlying assumptions about the distribution of data (unlike other algorithms like GMM, which assume a Gaussian distribution of the provided data), it is extensively applicable in real-life circumstances. An attribute-based previous data set (also known as training data) is provided to us, allowing us to classify locations into groups.

Advantages of the KNN Algorithm:

1. Easy Implementation: It is a straightforward algorithm to implement, making it a good choice for beginners.
2. Adaptability: The algorithm adapts easily to new examples or data points. Since it stores all the data in memory, when new data is added, it adjusts itself and incorporates the new information into future predictions.
3. Few Hyperparameters: KNN has few hyperparameters, namely the value of k (number of neighbors) and the choice of distance metric. This simplicity in parameter tuning makes it easy to use and experiment with different configurations.

Disadvantages of the KNN Algorithm:

1. Scalability Issue: Due to its “lazy” nature, KNN stores all the training data and compares it to every new data point during prediction. This makes it computationally expensive and time-consuming, especially for large datasets. It requires significant data storage for the entire training set, which becomes impractical with massive datasets.
2. Curse of Dimensionality: As the number of features (dimensions) in your data increases, the effectiveness of KNN drops. This phenomenon is known as the “curse of dimensionality.” In high-dimensional space, finding truly similar neighbors becomes difficult, leading to inaccurate classifications.
3. Overfitting: Due to the challenges with high dimensionality, KNN is susceptible to overfitting, where the model memorizes the training data too closely and fails to generalize well to unseen data .To mitigate this, techniques like feature selection and dimensionality reduction are often used, adding complexity to the process.

There are numerous machine learning algorithms available, each with its strengths and weaknesses depending on the scenario. Factors such as the size of the training data, the need for accuracy or interpretability, training time, linearity assumptions, the number of features, and whether the problem is supervised or unsupervised all influence the choice of algorithm. It’s essential to choose an algorithm carefully based on these factors. In this article, we will compare two popular algorithms, Decision Trees and K-nearest Neighbor (KNN), discussing their workings, advantages, and disadvantages in various scenarios.