KNN, or k-nearest neighbor algorithm, is an unhurried, non-parametric learning algorithm renowned for making predictions based on a multitude of classes. Being a non-parametric algorithm, KNN does not have any prerequisite or assumptions related to the data it works with. Instead, it learns and relies on the information directly provided through the data.
KNN stands out as a 'lazy' learning algorithm because it abstains from making any sweeping generalizations from the training data provided. Instead, it quickly processes and retains the entire data set for the testing phase, indicating a negligible or even non-existent training phase.
Traditional models, such as linear regression, often falter when dealing with data that deviate from theoretical assumptions. This is where the non-parametric nature of KNN shines. It can work accurately even when there's minimal or no prior data.
Supervised machine learning, such as KNN, depends on labeled input data to deduce a suitable output function when served with unlabeled data. This is done through training on a pre-labelled data set and then predicting the label for an untagged data point. For instance, diagnosing a tumor is done by training the algorithm with countless clinical results tagged as positive or negative. The well-trained model predicts the unlabeled test's outcome.
The upsides and downsides of KNN
- Quick computation time.
- Simplistic algorithm, easy to comprehend.
- Capable of both regression and classification.
- Exceptional accuracy compared to other supervised learning models.
- Independent of preliminary assumptions about data, eradicating the need for modification in multiple parameters or contriving a model.
- Data quality greatly impacts accuracy.
- Predictions can take longer with larger data volumes.
- It is sensitive to irrelevant dimensions and dataset size.
- High memory requirement due to retention of all training data.
- It may be computation-intensive as it stores all of the training.
KNN finds extensive use in corporations like Amazon and Netflix for personalised recommendations of books and movies. They use KNN to sift through data from your reading or viewing history on their platforms, and your preferences are juxtaposed with those of customers with similar tastes. Based on this comparison, books or movies are then suggested to you.
The K-nearest neighbor method retains all existing data and classifies new data points on the scales of similarity. It promptly groups new data into an appropriate category. In a two-class scenario, a new data point's class can be determined by the majority vote of its neighbors, where the data point is assigned to the most common class among its K closest neighbors.
KNN does not learn any model, but uses the similarity between input samples and each training instance to predict outcomes. KNN is a great first step when exploring models based on various datasets. To begin with KNN, one needs to initiate with a diverse and reliable data collection.