Never in the same shape
Inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.
Clustering is an unsupervised machine learning technique used to group similar data sets together to identify patterns or structures in the data space. The goal is to divide a data set into homogeneous groups, so that elements within the same group are more similar to each other than those in different groups.
XGBoost is an open-source library that has gained considerable popularity in the data science community for its effectiveness in solving a wide range of supervised machine learning problems. This library, primarily developed by Tianqi Chen, offers a powerful tree boosting algorithm that relies on successive iterations to improve model accuracy. One of its standout features is the ability to easily handle missing data during the training process, significantly simplifying the workflow for users.
LightGBM is an extremely powerful and versatile machine learning library that has quickly gained popularity in the data science and machine learning community. It began as an open-source project developed by Microsoft and has evolved to become one of the most used libraries for regression, classification and ranking problems.
In the modern data era, machine learning has become an essential component for extracting meaningful insights and data-driven decision making. In this article, we will explore the features and capabilities of the Scikit-learn library, a versatile and powerful tool for machine learning in Python. From data preparation to model building and performance evaluation, Scikit-learn offers a wide range of tools to tackle a variety of machine learning problems.
Sampling is a fundamental process in research and statistics, allowing meaningful conclusions to be drawn from a representative subset of a larger population. In this article, we will review the concept of sampling and the main methods used to select representative samples. Through practical examples in Python code and theoretical considerations, we will illustrate the importance of careful sample selection and the applications of different sampling methods.
In this article we will give a quick overview of the definition of mutually exclusive events, using some examples that can best elucidate these concepts, such as the launch d. In Python, as in other programming languages, it is easy to make simple programs useful for checking the exclusivity of events.
Longitudinal data in statistics refers to observations collected on the same study unit (for example, an individual, a family, a company) repeatedly over time. In other words, instead of collecting data from different study units at one point in time, you follow the same units over time to analyze the variations and changes that occur within each unit. In this article we will discover what they are and which study techniques to apply using Python as an analysis tool.
In the digital age we live in, Big Data has taken on a central role, radically transforming our understanding and management of information. In this section, we will explore the fascinating world of Big Data, from its fundamental role in the evolution of computing to the vast range of technologies used to manage and process it.
In the digital age we live in, the ever-increasing volume of data creates unprecedented challenges and opportunities for organizations in every industry. Big Data architecture and management strategies have become crucial elements to fully exploit the potential of this information wealth. In this article, we will explore the underlying architecture of Big Data and the key strategies for managing it effectively and efficiently.
To fully exploit the potential of Big Data, it is essential to be familiar with the technologies and tools that enable the collection, storage, processing and analysis of these enormous amounts of data. In this article, we will explore the landscape of leading Big Data technologies and tools, providing an in-depth overview of the solutions that are revolutionizing data management and analysis at scale.