# Feature engineering ## What is feature engineering? Feature engineering is the process of creating new features or modifying existing features in a dataset to improve the performance of a machine learning model. This step is crucial because the quality and relevance of the features used in the model can significantly impact its ability to learn and make accurate predictions. Feature engineering involves several techniques. *** ## Feature engineering techniques * Feature Selection - KBest * Dimensionality Reduction - SVD * Feature Extraction - TF-IDF(changing text features to number so they can be used in modules) * Feature Scaling * Feature Encoding * Feature Creation ## Are normalization & standardization the same thing? Normalization and standardization are similar in the sense that they both involve **rescaling the features of a dataset**. However, they are not the same thing and serve different purposes. **Formula:** $$Xnorm = \frac{X - X\_{\text{min}}}{X\_{\text{max}} - X\_{\text{min}}}​​$$ **Formula:** $$Z= \frac{(X - \mu)}{\sigma}$$ --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.