Here, you’ll find all articles that use synthetic data.
Synthetic data is generated by a computer program rather than being collected from the real world. It is often used in machine learning to train and evaluate models. In particular, when it is difficult or impossible to collect real-world data. Synthetic data can play an important role in machine learning. In particular, when real-world data is difficult to collect or is not suitable for training and evaluation. One of its applications is that it can provide a way to generate large quantities of data with specific properties. Furthermore, it can help protect privacy when working with sensitive data.
Embark on a journey into the world of unsupervised machine learning with this beginner-friendly Python tutorial focusing on K-Means clustering, a powerful technique used to group similar data points into distinct clusters. This invaluable tool helps us make sense of complex datasets, finding hidden patterns and associations without the need for a predetermined target variable. … Read more
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models does not exist. Instead, there … Read more
Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more
