Contents
Can you do KNN with categorical data?
KNN is an algorithm that is useful for matching a point with its closest k neighbors in a multi-dimensional space. It can be used for data that are continuous, discrete, ordinal and categorical which makes it particularly useful for dealing with all kind of missing data.
Why is scaling helpful before using KNN?
This will impact the performance of all distance based model as it will give higher weightage to variables which have higher magnitude (income in this case). Hence, it is always advisable to bring all the features to the same scale for applying distance based algorithms like KNN or K-Means.
Why do we need normalization in the KNN?
If the scale of features is very different then normalization is required. This is because the distance calculation done in KNN uses feature values. When the one feature values are large than other, that feature will dominate the distance hence the outcome of the KNN. Thanks for contributing an answer to Cross Validated!
Why do you need to scale data in KNN?
This is because the distance calculation done in KNN uses feature values. When the one feature values are large than other, that feature will dominate the distance hence the outcome of the KNN. Thanks for contributing an answer to Cross Validated!
How is the kNN algorithm used in classification?
KNN is one of the simplest forms of machine learning algorithms mostly used for classification. It classifies the data point on how its neighbor is classified. KNN classifies the new data points based on the s imilarity measure of the earlier stored data points. For example, if we have a dataset of tomatoes and bananas.
Do you need to check the accuracy of kNN?
The first model might have memorized more data due to some characteristic of that data, but it’s not a good thing. You would need to check your prediction accuracy on a different set of data than what was trained on, a so-called validation set or test set. Then you will know whether the KNN accuracy is OK or not.