Скачать или смотреть Cross-Validation using k-Nearest Neighbors In Python In Hindi/Urdu

In this video, you will learn how to implement k-nearest neighbors in python implementation.
This channel includes machine learning algorithms and the implementation of machine learning algorithms in Python.
Cross-Validation is used to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it.
Cross-Validation in Sklearn is very helpful for us to select the correct Model and Model parameters. By using that, we can intuitively see the effect of different Models or parameters on the structural accuracy.
We are going to use the famous dataset ‘iris’ with the KNN Classifier
Use KNN.score() to see the accuracy
Basically, the accuracy from KNN.score() only tests one set of train and test datasets.
Cross-Validation for Classification
In k-fold cross-validation, the original sample is randomly partitioned into k equal size subsamples.