RoshXUnique automatic machine learning technology

RoshX's automatic machine learning Automated machine learning (AutoML) enables machine learning to be automatically applied to practical problems. RoshX's automatic machine learning covers the whole process from the original data set to the deployable machine learning model. As a solution based on artificial intelligence, automatic machine learning technology is increasingly used to solve the growing challenges in machine learning applications. The high degree of automation in RoshX's automatic machine learning technology allows non-professionals to use machine learning models and techniques without becoming experts in the field.

In the practical application of RoshX's machine learning technology, automating the end-to-end process can produce more advantages: generating simpler solutions, creating these solutions faster, and often designing models that are better than manual designs.

RoshXAdvantages of automatic machine learning technology based on web

InRoshXIn a machine learning application, the user of the program uses a dataset of input data points for training. The program will use the corresponding data preprocessing, feature engineering, feature extraction and feature selection methods to make the data set suitable for machine learning. According to these preprocessing steps, the program will perform algorithm selection and hyperparameter optimization to maximize the prediction performance of their machine learning models and greatly simplify the application of non-professional machine learning.

RoshXAutomatic machine learning technology can be aimed at different stages of the machine learning process. In essence, this includes data preparation, feature engineering, model selection, evaluation index selection and hyperparameter optimization.

AndRoshXAutomatic learning technology can do: automatic data preparation, automatic data intention detection, automatic task detection, automatic feature engineering, feature selection, feature extraction, meta-learning and physical sign transformation, skew data and missing values detection and processing, automatic model selection.Feature Engineering and Learning algorithm'Hyperparameter optimization inAutomated pipelined selection under memory and complexity constraintsAutomatic selection of evaluation indicators/Verification programAutomatic problem detection, data leak detection, configuration error detection, automatic analysis of the results, user interface and visibility for automatic machine learning.