<p>Automated Machine Learning (AutoML) is a recent technology that provides speed to machine learning iterations and allows individuals with less experience to take advantage of existing tools. Due to several frameworks with different features, deciding the best option to solve each classification problem becomes difficult. It is necessary to consider aspects such as performance metrics and time when choosing the algorithm to reduce the demand for highly technical, specific knowledge in the subject. There are some comparisons of AutoML tools and approaches that perform tests in the area of data preprocessing, model selection, and hyperparameter optimization. However, most of these studies focus on binary and multiclass classification, not covering multilabel classifications and consequently not exploiting the full potential of the tools. In this paper, a comparative study between multiple AutoML tools is performed related to the features, architecture, capabilities, and results achieved on binary, multiclass, and multilabel classification problems from experimentation on various data sets.</p>

<p>Automated Machine Learning (AutoML) is a recent technology that provides speed to machine learning iterations and allows individuals with less experience to take advantage of existing tools. Due to several frameworks with different features, deciding the best option to solve each classification problem becomes difficult. It is necessary to consider aspects such as performance metrics and time when choosing the algorithm to reduce the demand for highly technical, specific knowledge in the subject. There are some comparisons of AutoML tools and approaches that perform tests in the area of data preprocessing, model selection, and hyperparameter optimization. However, most of these studies focus on binary and multiclass classification, not covering multilabel classifications and consequently not exploiting the full potential of the tools. In this paper, a comparative study between multiple AutoML tools is performed related to the features, architecture, capabilities, and results achieved on binary, multiclass, and multilabel classification problems from experimentation on various data sets.</p>

<p>Automated Machine Learning (AutoML) is a recent technology that provides speed to machine learning iterations and allows individuals with less experience to take advantage of existing tools. Due to several frameworks with different features, deciding the best option to solve each classification problem becomes difficult. It is necessary to consider aspects such as performance metrics and time when choosing the algorithm to reduce the demand for highly technical, specific knowledge in the subject. There are some comparisons of AutoML tools and approaches that perform tests in the area of data preprocessing, model selection, and hyperparameter optimization. However, most of these studies focus on binary and multiclass classification, not covering multilabel classifications and consequently not exploiting the full potential of the tools. In this paper, a comparative study between multiple AutoML tools is performed related to the features, architecture, capabilities, and results achieved on binary, multiclass, and multilabel classification problems from experimentation on various data sets.</p>

<p>Automated Machine Learning (AutoML) is a recent technology that provides speed to machine learning iterations and allows individuals with less experience to take advantage of existing tools. Due to several frameworks with different features, deciding the best option to solve each classification problem becomes difficult. It is necessary to consider aspects such as performance metrics and time when choosing the algorithm to reduce the demand for highly technical, specific knowledge in the subject. There are some comparisons of AutoML tools and approaches that perform tests in the area of data preprocessing, model selection, and hyperparameter optimization. However, most of these studies focus on binary and multiclass classification, not covering multilabel classifications and consequently not exploiting the full potential of the tools. In this paper, a comparative study between multiple AutoML tools is performed related to the features, architecture, capabilities, and results achieved on binary, multiclass, and multilabel classification problems from experimentation on various data sets.</p>

<p>Automated Machine Learning (AutoML) is a recent technology that provides speed to machine learning iterations and allows individuals with less experience to take advantage of existing tools. Due to several frameworks with different features, deciding the best option to solve each classification problem becomes difficult. It is necessary to consider aspects such as performance metrics and time when choosing the algorithm to reduce the demand for highly technical, specific knowledge in the subject. There are some comparisons of AutoML tools and approaches that perform tests in the area of data preprocessing, model selection, and hyperparameter optimization. However, most of these studies focus on binary and multiclass classification, not covering multilabel classifications and consequently not exploiting the full potential of the tools. In this paper, a comparative study between multiple AutoML tools is performed related to the features, architecture, capabilities, and results achieved on binary, multiclass, and multilabel classification problems from experimentation on various data sets.</p>