How to Leverage KNN Algorithm in Machine Learning?
Machine learning is the artificial intelligence part, which allows a machine to learn from the previous data without explicitly programming it. Machine learning teaches computer algorithms to upgrade using data and experience. KNN has been used in statistics for the estimation and pattern recognition already at the beginning of 1970, the method provided in several disciplines, and still, it is one of the top 10 data mining algorithms
What is KNN Algorithm
K-Nearest Neighbour Algorithm (KNN) measures the similarity or uniformity between the newly provided case and available facts and figures. It then classifies or groups it based on previously provided or available data. It is largely utilized in agriculture, text mining, finance, and healthcare. Like in the Healthcare sector, they apply the KNN algorithm to ascertain whether the patient is affected by certain diseases and conditions or not. You can also utilize it for regression, but users mainly prefer it for classification. In the simple language in KNN Algorithm, the classification of given data is dependent on its neighboring class. KNN is the non-parametric and easiest algorithm of machine learning. KNN is also known as memory-based reasoning, case-based reasoning, or example-based reasoning.
How KNN Algorithm Works
Let us now understand this in a better way with the help of an example: If we have a picture of a species like a turtle, we have to classify that either it is a turtle or rabbit. For solving such issues, we will use KNN Algorithm. It classifies the given species based on the previously provided data by matching or allocating the characteristics of the given species with the available data. In the end, it results/classifies the given species either as turtle or rabbit.
Strengths of KNN Algorithm
- It is very easy and intuitive
- It can apply to the data from any distribution
- If the number of samples is large, then good classification occurs.
Weaknesses of KNN Algorithm
- It will take more time if there is a new example for classifying
- As it needs to ascertain and compare the distance from a New example to another example
- Estimation of k may be tricky sometimes
- Accuracy occurs if there are a large number of data, so not appropriate for a small number of data.
Which Course To Choose
As discussed above, depending on your interest and knowledge, you can select the programs or courses that can offer you a complete learning experience. If you are interested and want to pursue a career in this amazing and exciting field, first, you will have to check out our AI and Machine Learning Courses. You can always use well-curated programs designed by experts in the Data Science field. These will involve using various algorithms based on your knowledge, so it should be relatively easy for you to pick up skills here.
You can check out the Post-Graduate programs in AI and Machine Learning from Great Learning o further your career in Data Science and related fields.