The following are the primary benefits of using kNN for classification: Implementation is very straightforward. In terms of the search space, it's robust; for example, classes don't have to be linearly separable. As new examples with known classes are supplied, the Classifier can be updated online at a low cost.
The KNN algorithms choose a number k as the closest Neighbor to the data point to be categorised. If k is set to 5, it will search for the 5 closest Neighbors to that data point. If we suppose k=4 in this case, KNN learns about the four closest Neighbors.
This one is one of the best online free source to study the coding or any particular course.
Course is nice but where is the link for installation of Anaconda
Course is good understandable but I am not able to download resources (one star less only for this not able to download resources)
I am impresses by the way of teaching, what a magical teaching skill he has.
good content with free of cost
Course content and explanation method is just awesome. I like the way they presenting and specially at the end of each video content they feeding next intro content which makes motivated, excited .
The courses are very useful and encourage learning. I highly appreciate that and thank the learnvern team very much indeed for the great and nice job.
Yuganun Ramlugun from Mauritius.
The course is explained in an excellent manner with easy interpretations and simple examples. Thank you very much Sir .Really Learnvern is doing great job offering help to many Indians.
Share a personalized message with your friends.