Here’s the tabular comparison with K-means, Hierarchical Clustering, and DBSCAN in the requested order: Aspect K-means Hierarchical Clustering DBSCAN Clustering Approach Partitioning Agglomerative or Divisive Density-based Shape of Clusters Spherical, equally sized Various shapes (depends on linkage) Arbitrary shapes Number of Clusters Requires specifying K beforehand No predefined K required…
Data clustering is a fundamental technique in the field of data science and machine learning. It involves grouping data points that are similar to each other. While many clustering algorithms exist, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) stands out as a robust method that can identify clusters of…
k-means clustering, like many machine learning algorithms, has hyperparameters that need to be set prior to running the algorithm. These hyperparameters affect how the algorithm works and can impact the quality of the clustering results. Here are some common hyperparameters in k-means: Number of Clusters (k): Perhaps the most crucial…
Clustering is one of the most common exploratory data analysis techniques used to get an intuition about the structure of the data. K-means clustering is one of the simplest and most popular unsupervised machine learning algorithms. k-Means Clustering is an algorithm that, given a dataset, will identify which data points belong to…
K-NN stands for K-Nearest Neighbors, and it’s a type of learning method where the algorithm doesn’t really “train” the data in the usual way. Instead, it memorizes instances from the training dataset and uses these instances directly to make predictions. It is known as “lazy learning”. It is a type…
In machine learning, handling missing values is a common challenge. Not all algorithms can handle missing values naturally, but some have been designed or adapted to do so. Here’s an explanation of a few such algorithms: Decision Trees and Random Forests: Decision trees inherently handle missing values. During training, if…
A support vector machine (SVM) is a supervised machine learning model which can be used for both classification and regression. But they have been extensively used for solving complex classification problems such as image recognition, voice detection etc. SVM algorithm outputs an optimal hyperplane that best separates the tags. The hyperplane is a boundary that…
A confusion matrix is a fundamental tool in the field of machine learning and data science, often used to assess the performance of classification models. It provides a detailed breakdown of the model’s predictions compared to the actual ground truth, allowing us to evaluate various aspects of model performance. The…
Introduction In the quest to understand relationships between variables, two terms consistently surface correlation and causation. Despite their apparent similarity, they have different implications and uses. This distinction is more than just a technicality; it’s a fundamental concept that every data analyst or scientist needs to grasp. The Basics of…
Feature scaling is an important technique in Machine Learning and it is one of the most important steps during the preprocessing of data before creating a machine learning model. The reason to perform features scaling is to ensure one feature doesn’t dominate others. The two most important scaling techniques are…