Category: Data Science

Weak Supervision

Weak supervision is a method of training machine learning models using data that is labelled with less accuracy or detail than traditional hand-labelled datasets. Instead of relying solely on expensive, manually created labels, weak supervision uses noisier, incomplete, or indirect sources of information. These sources can include rules, heuristics, crowd-sourced labels, or existing but imperfect…

Active Learning Framework

An Active Learning Framework is a structured approach used in machine learning where the algorithm selects the most useful data points to learn from, rather than using all available data. This helps the model become more accurate with fewer labelled examples, saving time and resources. It is especially useful when labelling data is expensive or…

Data Augmentation Framework

A data augmentation framework is a set of tools or software that helps create new versions of existing data by making small changes, such as rotating images or altering text. These frameworks are used to artificially expand datasets, which can help improve the performance of machine learning models. By providing various transformation techniques, a data…

Synthetic Data Generation

Synthetic data generation is the process of creating artificial data that mimics real-world data. This data is produced by computer algorithms rather than being collected from actual events or people. It is often used when real data is unavailable, sensitive, or expensive to collect, allowing researchers and developers to test systems without risking privacy or…

Feature Importance Analysis

Feature importance analysis is a method used to identify which input variables in a dataset have the most influence on the outcome predicted by a model. By measuring the impact of each feature, this analysis helps data scientists understand which factors are driving predictions. This can improve model transparency, guide feature selection, and support better…

Feature Engineering Pipeline

A feature engineering pipeline is a step-by-step process used to transform raw data into a format that can be effectively used by machine learning models. It involves selecting, creating, and modifying data features to improve model accuracy and performance. This process is often automated to ensure consistency and efficiency when handling large datasets.