How can feature engineering be streamlined for machine learning?

Our CEO Ryohei Fujimaki, Ph.D., recently shared his insights with TechTarget’s SearchDataManagement: https://bit.ly/2qM2CsQ #datascience #machinelearning #artificialintelligence

To improve machine learning, data scientists need structured data, and feature engineering is required to refine and clean that data to improve machine learning models. Data engineers can make feature engineering for machine learning processes easier by taking advantage of popular techniques and automating the operation to eliminate some grunt work. Feature engineering helps the machine learning processes by expanding and organizing the raw data set. A variable feature can influence the prediction models more than the raw data. When collecting raw data from multiple sources, bringing it into one place and storing it in a data lake or warehouse is the first step. The third step in machine learning is feature engineering, which involves validating, cleaning, and merging data to create a single source of truth for data analysis.

Data engineers combine raw data to identify which fields are most relevant for a particular machine learning problem. The combination is done by creating a correlation matrix, eliminating extreme values, and normalizing the values. Even Excel is potentially a feature engineering tool, but it is hard to go back and correct errors. Jupyter Notebooks or regular code files make it easy to document the process of feature engineering. IT teams and data scientists often work at odds. A data transformation platform can make it easier to run experiments. Feature engineering can help to generate features in unstructured data by using deep learning algorithms. The use of neural networks to build machine learning models can reduce the grunt work of legal research.

Specific featurization algorithms can work with messy text data by using word vectors, word vectors, and word vectors, robust to different types of noise. To improve machine learning models, create an error analysis pipeline for the back end of the process. Researchers at MIT released an open-source tool, Deep Feature Synthesis, in 2015, and several commercial products like dotData started to support automated feature engineering capabilities. Read the full article at TechTarget.