Designing adequate and precise neural architectures is a challenging task, often done by highly specialized personnel. AutoML is a machine learning field that aims to generate good performing models in an automated way. Spectral data such as those obtained from biological analysis have generally a lot of important information, and these data are specifically well suited to Convolutional Neural Networks (CNN) due to their image-like shape. In this work we present NASirt, an AutoML methodology based on Neural Architecture Search (NAS) that finds high accuracy CNN architectures for spectral datasets. The proposed methodology relies on the Item Response Theory (IRT) for obtaining characteristics from an instance level, such as discrimination and difficulty, and it is able to define a rank of top performing submodels. Several experiments are performed in order to demonstrate the methodology's performance with different spectral datasets. Accuracy results are compared to other benchmarks methods, such as a high performing, manually crafted CNN and the Auto-Keras AutoML tool. The results show that our method performs, in most cases, better than the benchmarks, achieving average accuracy as high as 96.96%.

--There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases. Automated Machine Learning (AutoML) promises major productivity boosts for data scientists, ML engineers and ML researchers by reducing repetitive tasks in machine learning pipelines. There are currently a number of different tools and platforms (both open-source and commercially available solutions) that try to automate these tasks. The goal of this paper is to address the following questions: (i) what are the available ML functionalities provided by the tools; (ii) how the tools perform when facing a wide spectrum of real world datasets; (iii) find the tradeoff between optimization speed and accuracy of the results; and (iv) the reproducibility of the results (a.k.a.

Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms. Current studies on deep learning mainly focus on data sets with balanced class labels, or unbalanced data but with massive amount of samples available, like in speech recognition. However, the capacities of deep learning on imbalanced data with little samples is not deeply investigated in literature, while it is a very common application context, in numerous industries. To contribute to fill this gap, this paper compares the performances of several popular machine learning algorithms previously applied with success to unbalanced data set with deep learning algorithms. We conduct those experiments on an highly unbalanced data set, used for credit scoring. We evaluate various configuration including neural network optimisation techniques and try to determine their capacities when they operate with imbalanced corpora.

Witnessing the data science field's meteoric rise in demand across pretty much all industries and areas of scientific research, it's easy to anticipate efforts to create shortcuts to satisfy the need for more data science practitioners. The current trend of automated machine learning is a great case in point. This article will touch on a number of efforts to circumvent the need for data scientists to select and train machine learning models and determine metrics for measuring their performance. The search for automated approaches in computer science is not new. I can remember as far back as the 1980s when the birth of the Personal Computer triggered a steep advance in the demand for programmers to develop software for the small machines.

As we already know, machine learning is a way of automating complex problem-solving. But can machine learning itself be automated? That's what we'll explore in this article. By its end, we'll have answered that question and shown practical ways it can be accomplished. When applying machine learning models, we'd usually do data pre-processing, feature engineering, feature extraction and, feature selection.

Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models. Note: currently, Auto-Keras is only compatible with: Python 3.6.

Automated machine learning is the new kid in town, and it's here to stay. It is helping us create better and better models with easy to use and great API's. AutoML is not automated data science. While there is undoubtedly overlap, machine learning is but one of many tools in the data science toolkit, and its use does not actually factor in to all data science tasks. For example, if prediction will be part of a given data science task, machine learning will be a useful component; however, machine learning may not play in to a descriptive analytics task at all.

Auto-Keras is an open source software library for automated machine learning (AutoML). The ultimate goal of AutoML is to allow domain experts with limited data science or machine learning background easily accessible to deep learning models. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models. Note: currently, Auto-Keras is only compatible with: Python 3.6. Here is a short example of using the package.