Benchmarking AutoML for regression tasks on small tabular data in materials design.Machine Learning has become more important for materials engineering in the last decade.  Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions.  Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples.  Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering.  The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. 
AutoML
Benchmarking AutoML for regression tasks on small tabular data in materials design.Machine Learning has become more important for materials engineering in the last decade.  Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions.  Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples.  Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering.  The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. 
AutoML
Benchmarking AutoML for regression tasks on small tabular data in materials design.Machine Learning has become more important for materials engineering in the last decade.  Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions.  Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples.  Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering.  The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. 
AutoML
Benchmarking AutoML for regression tasks on small tabular data in materials design.Machine Learning has become more important for materials engineering in the last decade.  Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions.  Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples.  Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering.  The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. 
AutoML
Benchmarking AutoML for regression tasks on small tabular data in materials design.Machine Learning has become more important for materials engineering in the last decade.  Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions.  Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples.  Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering.  The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. 
AutoML