Classification is a type of supervised machine learning problem where the goal is to predict the class or category of an input instance based on a set of features or attributes. In a classification problem, the target variable (i.e., …

Empirical Mode Decomposition (EMD) is a signal processing technique that decomposes a signal into its underlying oscillatory components, called Intrinsic Mode Functions (IMFs). It is a data-driven method that does not require prior knowledge of the signal’s frequency content or the characteristics of the underlying oscillations. The EMD algorithm involves…

Data segmentation in computer vision refers to the process of dividing an image or video dataset into smaller, more manageable subsets based on certain criteria. …

Light GBM (Light Gradient Boosting Machine) is a popular open-source framework for gradient boosting. It is designed to handle large-scale datasets and performs faster than other popular gradient-boosting frameworks like XGBoost and CatBoost. Light GBM uses a gradient-based one-sided sampling method to split trees, which helps to reduce memory usage…

In machine learning, a “splitter” refers to a tool or technique that is used to split a dataset into separate subsets for training and testing a machine learning model. Splitting a dataset is a common practice in machine learning to avoid overfitting the model to the training data and to…

In machine learning, a splitter is a function or module used to split a dataset into two or more subsets for different purposes. Splitting a dataset is an essential step in many machine-learning tasks, such as model training, validation, and testing. The most common type of splitter is the train-test…

Multitask learning (MTL) is a machine learning approach that involves training a model to perform multiple tasks simultaneously using the same input features. In other words, instead of introducing a separate model for each task, MTL trains a single model that can perform multiple tasks. The idea behind MTL is…

Parameters In machine learning, a model parameter refers to a configuration variable that is internal to the model and is used to make predictions on new data. Model parameters are learned from training data, which is input to a machine learning algorithm. The algorithm uses the training data to adjust the…

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) and computer science that focuses on enabling computers to process, understand, and generate human language. …