Two-label classification is a type of machine learning problem in which the goal is to predict which of two classes a given data point belongs to. This is a type of binary classification, which is a common and fundamental problem in machine learning. In a two-label classification problem, the data points are typically represented as a set of feature vectors, and the goal is to learn a model that can accurately predict the class label for any new data point based on its feature vector. This can be done using a variety of different algorithms, such as support vector machines or decision trees.
This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field of deep learning, a subarea of machine learning, and have become a cornerstone to many exciting innovations. There are endless applications, from self-driving cars over biometric security to automated tagging in social media. And the … Read more
Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be the key to maintaining long-lasting client relationships. Businesses incur significant costs when customers discontinue their services, hence it’s vital to identify potential churn risks and take preemptive actions to retain these customers. Machine Learning models … Read more
