How to streamline feature engineering for machine learning

For impactful machine learning, data scientists first need clean, structured data. That's where feature engineering comes in -- to refine data structures that improve the efficiency and accuracy of machine learning models. Ryohei Fujimaki, Ph.D., CEO and founder of dotData, a data science platform, said, "Features are, without question, even more critical than the machine learning algorithm itself." Poor quality features will result in a failure of the machine learning algorithm, he said. On the other hand, high-quality features will allow even simple machine learning algorithms like linear regression to perform well.