___ is the purpose of feature engineering in machine learningQuestion 1Answera.Building better hardwareb.Selecting the most relevant featuresc.Engineering new features using deep learningd.Extracting features from images

The purpose of feature engineering in machine learning is both b. Selecting the most relevant features and c. Engineering new features using deep learning.

Feature engineering is a process where we use domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms.

Selecting the most relevant features helps in improving the accuracy of the model, reducing overfitting, improving comprehensibility of the model, and reducing training time.

Engineering new features using deep learning involves creating new features from the existing ones, which can help in improving the model's performance. This can be done using various techniques like polynomial features, interaction features, etc.

Extracting features from images is a part of feature engineering but it's not the sole purpose of feature engineering. Similarly, building better hardware is not related to feature engineering. It's more about improving the computational efficiency and speed of machine learning operations.