The validation of a data-driven model is the process of asses sing the model's ability to generalize to new, unseen data in the population o f interest. This paper proposes a set of general rules for model validation. T hese rules are designed to help practitioners create reliable validation plans and report their results transparently. While no validation scheme is flawle ss, these rules can help practitioners ensure their strategy is sufficient for pr actical use, openly discuss any limitations of their validation strategy, and r eport clear, comparable performance metrics. Keywords: Validation, Cross-validation 1. Introduction Model validation is a fundamental task in all modern data-dr iven systems, whether they fall under the broad categories of Statistics, Machine Learning (ML), Artificial Intelligence (AI), or more specialized fiel ds like chemometrics. Validation has become a major focus for regulatory and stand ardization bodies, with key reports and standards highlighting the growing con cern for ensuring the trustworthiness and reliability of data-driven models: NIST AI Risk Management Framework (AI RMF 1.0, 2023): Publi shed by the U.S. Department of Commerce, this framework provides management techniques to address the risks and ensure the trustwor thiness of AI systems, with validation as a core component. The EU AI Act of 2024, landmark piece of EU legislation that c ategorizes AI systems by risk level, where validation is not defined as a b est practice but a legal requirement within the conformity assessment. The ISO/IEC TS 4213:2022, by the International Organizati on for Standardization (ISO), describes approaches and methods to ens ure the rele-Email address: josecamacho@ugr.es The IEEE P2841 -2022 is a recommended practice for the fram ework and process for deep learning evaluation.

Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rates are obtained especially when these algorithms are fine-tuned for the application at hand. Although this tuning process can require large computational costs, recent work has shown that these costs can be reduced by line search methods that iteratively adjust the step length. We propose an alternative approach to stochastic line search by using a new algorithm based on forward step model building. This model building step incorporates second-order information that allows adjusting not only the step length but also the search direction. Noting that deep learning model parameters come in groups (layers of tensors), our method builds its model and calculates a new step for each parameter group. This novel diagonalization approach makes the selected step lengths adaptive. We provide convergence rate analysis, and experimentally show that the proposed algorithm achieves faster convergence and better generalization in well-known test problems. More precisely, SMB requires less tuning, and shows comparable performance to other adaptive methods.

We explore the possibility of meta-learning for the language-independent unsupervised tokenization problem for English, Russian, and Chinese. We implement the meta-learning approach for automatic determination of hyper-parameters of the unsupervised tokenization model proposed in earlier works, relying on various human-independent fitness functions such as normalised anti-entropy, compression factor and cross-split F1 score, as well as additive and multiplicative composite combinations of the three metrics, testing them against the conventional F1 tokenization score. We find a fairly good correlation between the latter and the additive combination of the former three metrics for English and Russian. In case of Chinese, we find a significant correlation between the F 1 score and the compression factor. Our results suggest the possibility of robust unsupervised tokenization of low-resource and dead languages and allow us to think about human languages in terms of the evolution of efficient symbolic communication codes with different structural optimisation schemes that have evolved in different human cultures.

Collaboration on complex development projects almost always presents challenges. For traditional software projects, these challenges are well known, and over the years a number of approaches to addressing them have evolved. But as machine learning (ML) becomes an essential component of more and more systems, it poses a new set of challenges to development teams. Chief among these challenges is getting data scientists (who employ an experimental approach to system model development) and software developers (who rely on the discipline imposed by software engineering principles) to work harmoniously. In this SEI blog post, which is adapted from a recently published paper to which I contributed, I highlight the findings of a study on which I teamed up with colleagues Nadia Nahar (who led this work as part of her PhD studies at Carnegie Mellon University and Christian Kästner (also from Carnegie Mellon University) and Shurui Zhou (of the University of Toronto).The study sought to identify collaboration challenges common to the development of ML-enabled systems.

Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modeling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for the automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges representing the protein chain. Combining information from the voxel-based cryo-EM data, the amino acid sequence data, and prior knowledge about protein geometries, the GNN refines the geometry of the protein chain and classifies the amino acids for each of its nodes. Application to 28 test cases shows that our approach outperforms the state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5 Å Following rapid developments in microscopy hardware and image processing software, cryo-EM structure determination of biological macromolecules is now possible to atomic resolution for favourable samples (Nakane et al., 2020; Yip et al., 2020). For many other samples, such as large multi-component complexes and membrane proteins, resolutions around 3 Å are typical (Cheng, 2018). Transmission electron microscopy images are taken of many copies of the same molecules, which are frozen in a thin layer of vitreous ice. Dedicated software, like RELION (Scheres, 2012) or cryoSPARC (Punjani et al., 2017), implement iterative optimization algorithms to retrieve the orientation of each molecule and perform 3D reconstruction to obtain a voxel-based map of the underlying molecular structure. Provided the cryo-EM map is of sufficient resolution, it is interpreted in terms of an atomic model of the corresponding molecules. Many samples contain only proteins; other samples also contain other biological molecules, like lipids or nucleic acids.

Additionally, the chance is you won't be working with a dataset, so merging data is also a common operation you'll use. Extracting meaningful information from data becomes easier if you visualize it. In Python, there are many libraries you can use to visualize your data. You should use this stage to detect the outliers and correlated predictors. If undetected, they will decrease your machine-learning model performance.

A machine learning pipeline consists of multiple data extraction, preprocessing, and model-building steps. It helps to automate processes that are required in model building. Pipeline helps to include all steps of preprocessing, feature selection, feature extraction, model selection, and model building In one entity. Here we will see how to make a pipeline in machine learning. In Applications where you need multiple machine learning models then, in that case, this pipeline will help you a lot.

Welcome to Tidy Modeling with R! This book is a guide to using a collection of software in the R programming language for model building called tidymodels, and it has two main goals: First and foremost, this book provides a practical introduction to how to use these specific R packages to create models. We focus on a dialect of R called the tidyverse that is designed with a consistent, human-centered philosophy, and demonstrate how the tidyverse and the tidymodels packages can be used to produce high quality statistical and machine learning models. Second, this book will show you how to develop good methodology and statistical practices. Whenever possible, our software, documentation, and other materials attempt to prevent common pitfalls. In Chapter 1, we outline a taxonomy for models and highlight what good software for modeling is like.

When the eerily accurate AI image generator Dall-E 2 was released for public experimentation by OpenAI this summer, most people immediately used it to create whimsical scenes such as "samurai dolphin painted in the style of Rembrandt" or "Bruce Willis angrily devouring a cheeseburger on the moon". True, if you looked too closely at Bruce's left ear you might have noticed it wasn't there – but the freaky glitches were, though somewhat unsettling, part of the fun, not to mention a calming reminder that AI cannot entirely trick us that its images are real – yet. But more than one panicked architect also typed in, "Four-storey family home in forest in the style of Mies van der Rohe" or "Japanese-Scandi lounge area in office building lobby", and let out a tiny scream when the results resembled the renders of projects that architects otherwise spend long hours churning out. If an AI could knock out a decent interior in seconds, did it promise to be a fabulous time-saver – or would it put everyone out of a job? Not only does it celebrate the painstaking construction of physical structures, complete with tiny people and fake trees like a model railway set, which clearly took ages to make and no AI could come close to replicating – yet, but these models are also animatronic: they move, open, chirp, whirr, creak and close like Victorian clockwork figurines or the childlike works of Rodney Peppe.

Solutions Review's listing of the best big data science tools is an annual sneak peek of the top tools included in our Buyer's Guide for Data Science and Machine Learning Platforms. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials. The editors at Solutions Review have developed this resource to assist buyers in search of the best big data science tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process -- one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we've profiled the best big data science tools providers all in one place.