The KNN or k-nearest neighbors algorithm is a well-administered and foolproof algorithm for machine learning which is normally used to find solutions for regression and categorization problems. A supervised machine learning algorithm is heavily dependent on labelled input data to master a function. As a result of which it can appropriately produce relevant output when it has been administered with new unlabeled data.
What is supervised machine learning?
Let us understand this whole concept with an example. Suppose the computer you have is a child and you are the supervisor (teacher, guardian, or parent). Now you want to teach the child (computer) the shape, size, structure, etc. of a rabbit. You need to exhibit to the child (computer) several different pictures which may include some pictures of the rabbit and the rest of them are of other animals such as pigs, dogs, cats, squirrels, etc.
Whenever the picture of rabbits arrives, we can shout ‘rabbit’ and for the rest of the animals we can say ‘no, not rabbit’. After continuously doing the same with the child (computer), we can ask it after showing a picture ‘rabbit?’ and in most cases, it will answer correctly by saying ‘rabbit’ or ‘no, not rabbit’ based on the picture. This is what supervised machine learning is. The supervised machine learning algorithms are used to decipher regression or categorization problems.
In a classification problem, the output comes as a distinct value and there is no middle ground for the result. For example, ‘loves eating smash burger’ and ‘does not love eating smash burger’. The analogy used earlier to help the child (computer) identify a rabbit is one more example of any classification problem.
In an unsupervised machine learning algorithm, the input data do not have any labels which mean there is no one to help the child (computer) learn when it is making a mistake or when its answer is correct.
The basic distinction between supervised and unsupervised machine learning algorithms is that supervised algorithms can make predictions if it is given new unlabeled data but unsupervised algorithms try to understand the basic structure of the data and provide us with more comprehension.
What is the K-nearest neighbours algorithm?
The K-nearest neighbour or KNN algorithm surmises that look like things are available in its vicinity. To put it in more simple terms, the principle behind this algorithm is that homogeneous things are close to each other. With this exact concept, the KNN algorithm becomes very useful for regression and classification problems.
This algorithm apprehends the level of similarity (based on distance, closeness, or proximity) with the mathematical concepts we are familiar with, and then it measures the interval between two points on a graph. There are also different ways to measure the distances and depending on the problem you can change the methods. Nevertheless, the euclidean distance or straight line is an extremely familiar method.
How the KNN algorithm works
- First, you need to load the data
- Program the K with your selected amount of neighbours
- For every example in the data -
- Measure the interval between the current example and the query example from the data
- Include the index and distance of the example in an organised collection
- Now you need to sort the organised collection in ascending order depending on the distance (from smallest to biggest)
- Select the first K entries from the organised collection
- From chosen K entries obtain their labels
- In the case of classification, you can return the mode of the K labels
- In case of regression, you can return the mean of the K labels
Advantages of using the KNN algorithm
- The KNN algorithm is straightforward to apply.
- This algorithm is flexible and that is why it can be used for regression, search, and classification.
- You need to tune different parameters, construct a model or fabricate supplementary assumptions.
Drawbacks of using the KNN algorithm
The only drawback of the KNN algorithm is that it becomes slow along with the growth in the number of predictors, examples, or independent variables.
Application of KNN algorithms
Due to the KNN algorithm’s primary drawback of becoming moderate whenever the data volume rises, it is not suitable for the environment that requires fast prediction. Furthermore, multiple faster algorithms can provide more precise results to the classification and regression problems.
However, if you possess enough computing resources to efficiently handle the data for making predictions, then KNN algorithms can be extremely useful for your project.
To know more about KNN or any other supervised and unsupervised machine learning algorithm you can enroll in a professional machine learning course and pursue your career in the same field.