What is feature engineering and how does it impact model evaluation and validation?

FE is basically about transforming or engineering the raw data into a format which helps improve the ML model outcomes. Doing thorough exploratory data analysis using domain knowledge helps - some of the FE techniques are 1. Tagging outliers 2. Imputation 3. Variable transformations 4. Scaling of data (min-max, standard scaler) 5. Encoding of categorical columns 6. Binning or discretization etc.. Develop a strong cross validation technique like Kfold, stratifiedKfold etc., based on the business case and apply features to model in a sequential way and record the cross validation scores based on accuracy/precision (based on business objective). If the scores improve with FE then we can say a robust validation and evaluation is done.

Here is the one aspect of feature engineering out of many - when we work with a lot of data, sometimes different pieces of information (features) can be very similar to each other. This similarity is not helpful and can make things more complicated than they need to be. So, in feature engineering, we check how closely related these pieces of information are. If we find that two or more pieces are almost the same (highly correlated), we keep just one and get rid of the others. This helps in making the data simpler and clearer for the models we use. By doing this, we can save time and effort when training these models. It's like cleaning up and organizing data so that the models can learn better and faster.

Feature engineering is the art of transforming raw data to enhance machine learning model performance. It involves tweaking, creating, or selecting features to improve the model's pattern recognition. Effective feature engineering directly impacts model evaluation and validation. Well-crafted features boost accuracy and robustness, while poorly engineered ones introduce noise, hindering performance. Think of it as sculpting raw data into a form that empowers your models to reveal meaningful insights. It's not just about building models; it's about optimizing the very material they work with.

Feature engineering plays vital role in machine learning to optimize model performance. By carefully selecting, transforming, or creating features from raw data, it enables the extraction of relevant information, reduces noise and redundancy, improves computational efficiency, enhances model interpretability, and addresses data limitations. This process ensures that machine learning models are well-prepared to make accurate predictions on new data, contributing to their effectiveness and robustness.

Feature engineering is one of the best ways to make your model leaner. It is the optimization of the data you pass through it. As a simple example, if you were to create a classifier with over 100 hundred features, you would hopefully get an amazing F1 score with the trained model. However, there will be a point with the trained model where its performance reaches saturation, and adding new features will cease to improve it. The goal of feature engineering is find this point (i.e. having the best performance with the fewest features), as 1) making the model process information beyond this about each example can add noise and potentially negatively affect performance, 2) and increase inference time needlessly.

1. Explore & Understand: - Know your problem and target. - Analyze data, identify missing values, outliers, and types. 2. Transform: - Handle missing values and outliers. - Encode categorical features. - Scale features to prevent biases. 3. Create: - Use domain knowledge and input from subject matter experts to build new features. - Extract hidden patterns with techniques like PCA. - Select relevant features for better performance. 4. Evaluate & Iterate: - Train models with and without engineered features. - Continuously experiment and refine your approach.

Feature engineering includes many steps, but some are pretty common: 1. Dealing with Missing Values: Choices between removal based on completeness or distribution. 2. Handling Outliers: Managing them through removal or transformation based on their significance. 3. Feature Scaling: Ensuring uniformity and reshaping data for improved model performance. 4. Handling Numerical and Categorical Data: Strategies like encoding, binning, splitting, and transformations. 5. Feature Transformation: Applying mathematical operations for better data distribution. 6. Feature Selection: Using filter, wrapper, or embedded techniques for variable selection. 7. Dimensionality Reduction: Utilizing PCA, Randomised PCA, or other methods to simplify data.

To perform feature engineering, we need to: 1) understand the model, 2) understand the data, and 3) know the methods available to transform variables and how they affect the transformed variable and its relationship with the rest of the dataset. Different machine learning models require different transformations. I.E., linear models make assumptions over the data, and when these are not met we apply mathematical transformations or discretization. Linear and distance based models require scaling, tree based models do not. Some, but not all, implementations of tree based models tackle categorical variables under the hood. For the rest, we need some sort of encoding. Since data preprocessing is key, it's worth spending on it.

Feature engineering is all about working with data to make it better and more useful for achieving our goals. Here are some ways to do it. EDA- We look at how the data is spread out. If some data doesn't change much (low variance), it might not be very useful for our models, so we can consider removing it. Dealing with Missing Values: If a lot of data is missing in a certain area and we can't fill it in a way that makes sense (like using average values), it might be better to just remove these parts. Creating New Features: Sometimes, we can add new feature from what we already have. e.g. from a birthdate, we can calculate age or we can transform existing features into new feature. There are many other methods too.

Feature Engineering is one of the important part of ML models implementation process. It can help in visualization of standardized data and optimisation of results. Feature engineering helps to construct new features with the combination of multiple features in the dataset and thus, it can even be used for dimensionality reduction.

Comparing model accuracy, error and the presence of overfitting or underfitting before and after feature engineering is crucial for effective evaluation. This comparison reveals whether the newly added features are valuable. Additionally, plotting the importance of each feature offers insights into their contribution to the model's predictions, aiding in the understanding of the impact of newly engineered features on the model's performance. Performing cross validation before and after feature engineering helps in comparing robustness and stability of the model.

What does it mean "evaluate feature engineering"? Evaluate if the transformation returned the expected result? Evaluate if the transformation improved the model performance? Evaluate if the transformation is still interpretable? Vague questions can only get vague answers. I think the key here is to understand what we need to evaluate. First, if our code has no bugs, that is, if the transformation returned what we expect. Plots help. Second, model performance: use different transformations and compare performance metrics after transforming the data. Third interpretability: can I understand the feature after I transformed it? One hot encoding returns interpretable features, feature hashing does not.

Evaluating feature engineering involves assessing its impact on model performance. Metrics such as accuracy, precision, recall, and F1 score can be used before and after applying feature engineering to gauge improvements. Additionally, cross-validation and comparing performance on a validation set help ensure the robustness of the feature engineering process.

By generating or altering input features, feature engineering improves models. Use cross-validation to assess its influence by contrasting measures with and without designed features, such as accuracy or precision. Keep an eye on the model's complexity to prevent overfitting and make sure the characteristics you've chosen provide meaningful information. This evaluation guarantees enhanced model robustness and performance.

Evaluating feature engineering is a dynamic, iterative process crucial for model enhancement. Employ cross-validation, metrics, and visualization to gauge its impact on accuracy, computational efficiency, and time. Cross-validation entails dividing data into subsets for model training and testing. Metrics, including accuracy, precision, recall, F1-score, mean squared error, or mean absolute error, quantitatively compare model predictions with actual outcomes. Visualization, using tools like histograms, scatter plots, box plots, heat maps, or confusion matrices, offers insights into feature distribution, correlation, importance, and overall model performance.

Feature engineering steps depends on which ML algorithm we are using. To build optimised Feature Engineering pipeline we should have strong domain knowledge, knowledge of each feature in data and it's importance. Based on that we should select best feature engineering steps. For example, If using distance based algorithms, we should use feature scaling to avoid biasness of feature with high magnitude, If using outliers sensitive algorithms we should handle outliers using optimised strategy like mean/median/mode/capping/flooring. To handle missing values we can use advanced methods like iterative imputation or KNNImputer To handle imbalance data we can use advanced methods like SMOTomek, SMOTENN.

1) My personal framework to improve feature engineering: Domain Knowledge integration, Automation Tools and Advanced Technique from paper research in Arxiv 2) Domain Knowledge Integration: Bring an expert to examine your features and hypothesis to the target model. You will be surprised that expert able to think a new features from the dataset or even give suggestion to consider additional external features. 3) Automation Tools: Featuretools and PyCaret are nice libraries to start. Especially on the Deep Feature Synthesis. 4) Advanced Technique: Many research like DIFER, MACFE, Statistically Enhanced Learning, where combine the latest Deep Learning and approach to improve features engineering.

Rather than a cookie-cutter approach, feature engineering calls for imagination, expertise in the relevant domain, and trial and error. Applying new methods, trying out novel combinations, or incorporating additional data sources are all approaches to enhance your feature engineering. For instance, with tools like scikit-learn, pandas, or TensorFlow, you may automate workflows to apply various feature engineering procedures and strategies in a consistent and effective manner. Feature engineering for linear models, tree-based models, and deep learning models are just a few examples of the ways you may use to build and optimize your feature engineering process. Lastly, to make sure your feature engineering is reliable and high-quality

There are plenty of Python libraries that help you streamline feature engineering: Scikit-learn and Feature-engine support a broad range of methods for tabular data. Category encoders supports most methods for categorical encoding. tsfresh extracts features from time series for classification. Featuretools extracts features from transactions for classification or regression. Some forecasting libraries now support the creation of basic features from lags and windows, eg skforecast and sktime. The list is growing. Stay tuned.

Apart from all above, understand your industry for unique insights. Capture time-related patterns and explore feature interactions. Create features related to what you're predicting, like past trends. Use techniques like Heat Map to find correlations and Random Forests to check feature importance. For complex data, try advanced methods like embeddings. Transform data to capture hidden relationships. Ensure your features help the model generalize to new data and look for ethical concerns. Keep updating your strategies as you learn more or as the data changes. It's an ongoing process to keep your model effective and relevant to the current data flowing in the model.

1) Features engineering is a way to convert the raw data into a new set of features to understand the underlying problem, which results in better model performance, reduced computation, prevention of overfitting, and better interpretability (related to regulation). 2) Spending 60% or more time doing data preprocessing feature engineering is common in practice. The concept of garbage in and garbage out is very real here. As scientist, is important to provide data with high quality into the model 3) Leveraging the automation tools and domain expert in early process I found saving a lot of time when dealing with project timeline

There are multiple python and R packages which can help in feature engineering. I used a few based on the priority of these features we can reduce/remove it.