Calculate Similarity -- the most relevant Metrics in a Nutshell

Many data science techniques are based on measuring similarity and dissimilarity between objects. For example, K-Nearest-Neighbors uses similarity to classify new data objects. In Unsupervised Learning, K-Means is a clustering method which uses Euclidean distance to compute the distance between the cluster centroids and it's assigned data points. Recommendation engines use neighborhood based collaborative filtering methods which identify an individual's neighbor based on the similarity/dissimilarity to the other users. In this blog post I will take a look at the most relevant similarity metrics in practice. Measuring similarity between objects can be performed in a number of ways.