From playlist k-Nearest Neighbor Algorithm
k nearest neighbor (kNN): how it works
[http://bit.ly/k-NN] The k-nearest neighbor (k-NN) algorithm is based on the intuition that similar instances should have similar class labels (in classification) or similar target values (regression). The algorithm is very simple, but is capable of learning highly-complex non-linear decis
From playlist Nearest Neighbour Methods
k-NN 4: which distance function?
[http://bit.ly/k-NN] The nearest-neighbour algorithm is sensitive to the choice of distance function. Euclidean distance (L2) is a common choice, but it may lead to sub-optimal performance. We discuss Minkowski (p-norm) distance functions, which generalise the Euclidean distance, and can a
From playlist Nearest Neighbour Methods
k-NN 2: classification and regression
[http://bit.ly/k-NN] The k-NN algorithm operates as follows. For a new test instance, we first compute its distance to all the N training instances, and keep a small number k of nearest neighbours. For classification, we then predict the most dominant class among the k neighbours. For regr
From playlist Nearest Neighbour Methods
Creating Our K Nearest Neighbors Algorithm - Practical Machine Learning with Python p.16
Now that we understand the intuition behind how we calculate the distance/proximity between feature sets, we're ready to begin building our own version of K Nearest Neighbors in code from scatch. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprog
From playlist Machine Learning with Python
AQA Decision 1 8.04 The Travelling Salesperson Problem: The Nearest Neighbour Algorithm
I work through an example of the Nearest Neighbour Algorithm using a matrix and go through what to look out for.
From playlist [OLD SPEC] TEACHING AQA DECISION 1 (D1)
AQA Decision 1 8.06a The Travelling Salesperson Problem ex2: Nearest Neighbour Algorithm
I work through a second example of using the Nearest Neighbour Algorithm, this time without writing anything on the matrix.
From playlist [OLD SPEC] TEACHING AQA DECISION 1 (D1)
KD tree algorithm: how it works
[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data in
From playlist Nearest Neighbour Methods
[http://bit.ly/k-NN] What value of k should we use for the k-NN algorithm? Small values of k lead to a low-bias, high-variance classifier that can overfit to the training data. High values of k reduce the variance, but can lead to significant bias (a classifier that is inaccurate on the tr
From playlist Nearest Neighbour Methods
[http://bit.ly/k-NN] k-NN algorithm is computationally expensive because we need to compute the distance of each testing instance from every training instance. There is no exact algorithm for doing this quickly, but we do have approximate methods: K-D trees for low-dimensional data, invert
From playlist Nearest Neighbour Methods
KNN Classification & Regression in Python
#knn #machinelearning #python In this video, I've explained the concept of KNN algorithm in great detail. I've also shown how you can implement KNN from scratch in python. For more videos please subscribe - http://bit.ly/normalizedNERD Support me if you can ❤️ https://www.paypal.com/pa
From playlist ML Algorithms from Scratch
AQA Decision 1 8.05 The Travelling Salesperson Problem: The Lower Bound Algorithm
I work through a first try at the Lower Bound Algorithm and discuss what our result means alongside the upper bound given by the Nearest Neighbour Algorithm
From playlist [OLD SPEC] TEACHING AQA DECISION 1 (D1)