What is feature engineering and how can it improve your machine learning models?

Feature engineering is a critical aspect of data science, involving the transformation of raw data into meaningful features to enhance machine learning model performance. Start by understanding the relationship between variables and their impact on the outcome. Look for patterns or trends that can be captured as new features, while considering variable scale and distribution. When handling missing or incomplete data, employ techniques like imputation or creating new categories. Beware of overfitting, a scenario where a model performs well on training data but poorly on new data. Regularly validate your model's performance on a separate validation set. Be cautious of adding too many features that may not add meaningful information.

Features are calculation, formulas that add layers of interpretation to the data. A very simple one is to transform a percentage to a logarithm to improve the explainability of a model. A more complex one could be an if statement. Feature engineering is the way a data scientist adds value as an expert to the data.

Features are the variables that affect your decision-making process. The composition of these features depends on the specific business problem you are addressing, and they are often related to user behavior metrics when interacting with content or making purchases through an app or website. Features are created from the user's digital footprints, as well as characteristics and demographics, among other factors.

Feature engineering is the process of selecting, transforming, or creating relevant new features for a machine learning model to improve its performance. It can improve ML models by enhancing their ability to capture patterns and relationships in the data, resulting in better predictive accuracy and more robust generalization.

In the intricate world of machine learning, features serve as the backbone of predictive models. These features are the vital characteristics or attributes that define your data, shaping the predictions made by machine learning algorithms. For instance, when forecasting a house's price, features like size, location, age, and condition act as the essential components guiding the prediction process. Features, whether numerical, categorical, or text-based, encapsulate the core information within the dataset. In essence, understanding and crafting features are akin to unlocking the secrets buried within data, laying the foundation for accurate and meaningful machine learning outcomes.

Feature engineering plays a crucial role in accurately capturing the behavior of the target you aim to predict. The effectiveness of your machine learning model hinges on how well you represent your problem. Moreover, real-world data is often noisy, particularly in the case of high-frequency events. Much time is dedicated to data cleaning and variable creation. This process involves identifying the factors that truly influence the target variable, aligning them with what the business observes, and ensuring the correct features are input into the model. The more impactful variables you create, the more accurately your machine learning model can elucidate the variations within the data.

Being able to add insightful and useful feature engineering is what distinguish a data scientist that only knows fancy statistics to those who knows the subject matter of the dataset.

Think intuitively, what are the possible reasons affecting my task in hand. And feature engineering is all about quantifying those reasons mathematically into your model so that your objective can be achieved with the help of machine learning algorithms.

Perform feature engineering by analyzing the data, selecting relevant features, transforming variables, handling missing values, and creating new informative features based on domain knowledge and experimentation.

Feature engineering starts with applying domain knowledge to explore the features that are relevant. EDA to identify the patterns, handling missing data, treating categorical and scaling the numerical features are some of the processes to act upon on the initial steps of feature engineering. Could be proceeded with feature selection, analysis of important features identified depending on specific problem/ dataset. Also need to be cautious by implementing cross validation and regularisation techniques to prevent overfitting or data leakage issues during feature engineering.

Best practices for feature engineering include understanding the problem domain, exploring the data, avoiding overfitting, using domain knowledge, and iteratively testing and refining feature engineering techniques to enhance model performance.

Examples of feature engineering include creating polynomial features, one-hot encoding categorical variables, scaling numerical features, and generating new features like ratios, aggregations, and interactions between existing ones.

Let us consider an example from sales data of a retail outlet. Suppose the data has two columns -Customer ID and Transaction date. Now, separately these two columns may not convey much useful information, but from the Transaction date columns we can engineer a new column "Frequent Customer" based on the frequency of purchases of the customer. This new column can help you direct specific offers, recommendations or discounts to the customers based on their purchase frequency.

While one should focus on finding the independent features or deduced features (like, from two or more features), he should also be careful so to eliminate redundant features. It helps the model to be simpler & cost effective. Domain knowledge plays an important factor here. Also, one should be within ethics while choosing features. e.g., gender biased features shouldn't be choosen in general (in medical use cases though it might be required).