Feature Engineering:
- Feature engineering involves creating new features or modifying existing features to improve the performance of machine learning models.
- This can include tasks such as combining existing features, creating new features based on domain knowledge, and transforming features to improve their relevance or importance for the task at hand.
- The goal of feature engineering is to extract the most relevant and informative features from the data to help the model make accurate predictions.
Difference between Data Preprocessing & Feature Engineering:
| Data Preprocessing | Feature Engineering | |
| Refers to the initial steps taken to clean and prepare raw data before it can be used by machine learning algorithms. | It is a more specialized step that comes after or alongside data preprocessing. | |
| Aims to clean and standardize the raw data, making it suitable for analysis. | Aims to enhance the dataset by creating new, more informative features or transforming existing ones to improve model performance. | |
| Often the first step in data preparation and is generally applied to the raw data. | Typically performed after preprocessing and may require iterative experimentation. | |
| Deals with the entire dataset, ensuring it is in a suitable format for analysis. | More focused on the features (variables) themselves, enhancing their ability to capture the underlying patterns in the data. |
To be updated later…..
