Hierarchical clustering is a type of unsupervised machine learning algorithm used to cluster unlabeled data points. Like K-means clustering, hierarchical clustering also groups together the data points with similar characteristics. In some cases the result of hierarchical and K-Means clustering can be similar. Before implementing hierarchical clustering using Scikit-Learn, let’s first understand the theory behind hierarchical clustering. Theory of Hierarchical Clustering There are two types of hierarchical clustering: Agglomerative and Divisive. In the former, data points are clustered using a […]

Introduction A typical machine learning process involves training different models on the dataset and selecting the one with best performance. However, evaluating the performance of algorithm is not always a straight forward task. There are several factors that can help you determine which algorithm performance best. One such factor is the performance on cross validation set and another other factor is the choice of parameters for an algorithm. In this article we will explore these two factors in detail. We […]