Every time we mention machine learning, we have to follow up with feature engineering. You can never separate machine learning and feature engineering. That is how important feature engineering is.
Feature engineering is the process of selecting, modifying and creating new data features from existing or available data features to improve the performance of your machine learning model. In this guide, we will break down the basics of feature engineering in the easiest way possible.
Probably, most of us will ask the question why create new features from existing features? We can all agree that data is often vast, varied, and messy. It contains different data types and numerical values that span a wide range. We aim to standardize all features to make them uniform and suitable for creating a machine learning model. Feature engineering is the ultimate savior.
Without great features, even the best machine learning models may struggle with accuracy and precision. We engineer our features to ensure:
- Features are compatible with data: Having the right features in the correct form, including vectorizing our features, ensures our data is compatible with the model in use.
- Reduce overfitting: If our features do not equally contribute to the model, it can cause skewness. We aim to avoid this.
- Improve model performance: The model can better learn any underlying patterns in the dataset.
- Understand your data: Contextualize your data to determine which features are most essential and ensure your data types align with your needs.
- Handle missing data: Implement techniques to address missing or null values based on the context of your data.
- Feature selection: Identify relevant columns to the target variable using methods like correlation analysis.
- Feature transformation: Scale data to ensure equal contributions and avoid skewness in the model.
- Encoding: Convert categorical values into numerical values for modeling purposes.
Several tools can help with feature engineering:
- Pandas: A Python library great for data manipulation and analysis.
- Scikit-learn: Popular for machine learning in Python, offering preprocessing, model selection, and evaluation tools.
- Featuretools: An advanced tool to automate feature creation.
Feature engineering is a powerful skill in the machine learning toolkit. By carefully selecting and transforming features, you can improve the accuracy and reliability of your models. Gain experience, experiment with your data, and witness your models thrive!