How can you use feature engineering in machine learning?

I would like to emphasize the importance of understanding the domain knowledge in detail. Domain knowledge can provide context and intuition about what features might be relevant or irrelevant, how features might interact with each other, or what transformations might be appropriate. This can greatly improve the efficiency of your feature engineering and the quality of your features. For example, in a machine learning project related to healthcare, understanding medical terminologies, clinical procedures, disease progression, etc., can help you create more meaningful and predictive features from the raw health data.

Choosing variables and transforming them creatively into useful attributes or features for a particular use case to train a model is called feature engineering. It can be really useful to enhance the efficiency for predictive models as it might include isolating significant information, highlighting patterns, constructing relevant features based on domain expertise. For instance, if we wish to predict price of a new model of electric vehicle, the target attribute might be its market value. Some good domain knowledge based questions here could be would torque and horsepower be two separate parameters or they can be combined. Another good question might be whether comfort of seats and number of seats in the vehicle should be a factor/variable

Feature engineering is critical , transforming data into insights, enhancing algorithm performance. It requires domain expertise and creativity to extract relevant patterns. For example, in sentiment analysis, engineered features like sentiment-related keywords boost emotion prediction accuracy. Creating new features, like predicting returns based on purchase-sale price differences, showcases its practicality. It blends art and science, using domain knowledge, statistical analysis, and creativity to capture data relationships. However, effectiveness varies based on the dataset and problem. In conclusion, feature engineering empowers algorithms, demanding expertise, statistical insights, and creativity while being mindful of limitations.

This forms a key step in preparing, transforming and extracting features from raw variables based on domain knowledge to provide the best inputs for a machine learning model. There are different techniques of feature engineering. It could be handling outliers, noise, duplicates, incorrect data, missing values. It could also involve standardization to handle attributes of varied units. It could also involve encoding to handle different kinds of data (categorical/numerical). It could also involve combining different raw variables to make features that might make more sense to a particular use case.

For each use case, feature engineering involved might differ based on specificity of domain and attributes available. Some of the common feature engineering strategies used are: handling outliers, missing values, duplicates, noise; standardization for handling variables with varied units; normalization; encoding; feature extraction ( for example, Principle Component Analysis); feature selection and feature iteration.

For example, in order to enhance fraud detection in a financial transaction system,a new feature can be created to compare the current transaction amount with previous transactions of customers belonging to similar segment. If the current transaction amount significantly differs from the historical statistics of the corresponding customer segment, it may indicate a potential fraud attempt. In such cases, the transaction can be flagged for further investigation or requesting additional authentication from the customer.

In my customer churn project, I employed interactive features and binning. I developed interactive features by combining aspects like 'phone service' with 'multiple lines' and 'payment methods' with 'paperless billing', capturing specific customer behaviors. Binning was used to categorize ages into segments like '18-30', '30-65', and '65+' rather than treating it as a continuous variable, thus capturing non-linear effects for better churn prediction.

When it comes to optimizing feature engineering, domain knowledge is your North Star. It guides decisions on which features to create, ensuring they are both meaningful and interpretable. Keeping features simple is key to preventing overfitting and maintaining model effectiveness. Documenting the process is crucial for reproducibility and communication. Lastly, don't shy away from experimentation; it's a vital part of refining your feature set and improving model performance. In summary, leverage domain knowledge, prioritize simplicity, document diligently, and embrace experimentation to enhance your feature engineering endeavors.

I believe some techniques in Natural Language Processing (NLP) should also be considered as feature engineering, techniques like TF-IDF (Term Frequency-Inverse Document Frequency), word embeddings (such as Word2Vec and GloVe), sentiment scores (Liu Hu, Vader, LiLaH, Bing, NRC, AFINN, among others). In addition to those, the list should include others like stemming, lemmatization, getting N-grams, among others. Furthermore, I belive Binning of numerical variables is also a kind of feature engineering, which involves dividing continuous variables into discrete intervals, for example converting credit scores into bins like 'Excellent', 'Very Good', 'Good', "Not Good", and "Poor", based on predefined credit score ranges.