You're faced with complex feature engineering tasks. How do you prioritize quick results for stakeholders?

When tackling complex feature engineering tasks, I focus on delivering quick results for stakeholders by zeroing in on the most impactful features first. I rely on domain knowledge and quick exploratory analysis to identify these. I then use an iterative approach, creating and testing features in small batches to get rapid feedback. Automated tools streamline the process and save time. I keep stakeholders in the loop with regular updates, ensuring they see progress and stay engaged. This way, I balance speed and effectiveness, delivering meaningful results efficiently.

To begin with, have in-depth conversations with the stakeholders to comprehend their requirements, deadlines, and expectations. Clearly state the project's main goals and the business objectives. Decide on precise, measurable measures that will be used to gauge the project's performance. The creation of features will be guided by these indicators, which will also assist prioritize which features are most important.

You never directly move a mountain , you break it into smaller chunks. To tackle complex projects, break them into manageable chunks. Here' a 5 point plan to handle this: 1. Break Down the Project: Identify key components and categorize tasks. 2. Engage Stakeholders: Collaboratively prioritize tasks (P0's) with stakeholders to align objectives. 3. Develop a Plan: Set milestones and allocate resources for high-priority tasks. 4. Build and Deliver the MVP ( or as I call it MLP- Minimal Lovable Product) : Aim for a Minimal Lovable Product (MLP) that delights users, fostering early adoption. 5. Continuous Review and Adaptation: Hold regular check-ins and adapt the plan as needed.

To prioritize quick results for stakeholders when faced with complex feature engineering tasks, follow these steps: Identify Key Objectives: Focus on the features most critical to the stakeholders' goals. Leverage Existing Tools: Use automated feature engineering tools and libraries to expedite the process. Start Simple: Begin with straightforward, high-impact features that can be quickly implemented and evaluated. Iterative Approach: Deliver results incrementally, allowing for feedback and adjustments. Parallel Processing: If possible, work on multiple feature sets simultaneously to maximize efficiency.

One strategy to help facilitate this is to embed yourself or your team with your stakeholders. Develop a better understanding of the things that they actually have control over, and ensure those things can be informed or assisted by your model. I have been asked several times by junior colleagues why do I think their project wasn't received well, and I think my first question to figure this out has always been how much time did you spend with your stakeholders to understand how it would be used. This has usually been what I could identify as why a project wasn't well received or adopted.

🔍 Identify key features with the highest impact. 📈 Focus on features that offer quick wins. 🛠️ Use automated tools to streamline the process. ⏱️ Set clear, short-term milestones for progress. 🤝 Communicate regularly with stakeholders on updates. 📋 Implement agile methods for rapid iteration. 🚀 Prioritize features based on stakeholder feedback. ✅ Continuously evaluate and adjust priorities for optimal results.

While developing a predictive model for customer churn, I initially included numerous features from our dataset. However, the model's performance was underwhelming. I decided to evaluate each feature's impact using domain knowledge and statistical methods. By calculating correlations and leveraging insights from the marketing team, I identified key predictors like customer engagement and support interaction frequency. Prioritizing these high-impact features, I simplified the model, resulting in a significant accuracy boost. This experience underscored the importance of evaluating feature relevance early on to enhance model performance efficiently.

Develop a library of reusable scripts and functions for common feature engineering tasks. This approach not only accelerates development but also promotes consistency and reduces errors across projects. And moreover it can be use for the new projects.

"The goal is to obtain a workable set of features without getting bogged down in unnecessary complexity." Not really. The goal is to fix the problem without writing *any* software. Unnecessary complexity is assuming you're even writing the software. The best code is no code. By starting with no code solutions, paper prototypes and roughly sketched ideas you can prove the value quickly. Then simplify.

Para simplificar suas tarefas de engenharia de recursos e alcançar resultados rápidos, comece com transformações básicas. Utilize técnicas automatizadas de seleção de recursos para identificar aqueles mais promissores. Reaproveite recursos de projetos anteriores semelhantes e aplique estruturas de engenharia de recursos amplamente reconhecidas para acelerar o processo. O foco é obter um conjunto viável de recursos sem se perder em complexidades desnecessárias.

When faced with complex feature engineering tasks, prioritize quick results for stakeholders by simplifying your approach. Start by focusing on the most impactful features, using domain knowledge to identify key variables. Employ automated techniques like basic imputation, normalization, and one-hot encoding to streamline preprocessing. Leverage feature selection methods, such as correlation analysis or tree-based importance scores, to reduce dimensionality. Simplifying tasks not only accelerates the process but also enhances model interpretability and performance, allowing you to deliver meaningful insights rapidly while maintaining the quality of your analysis.

Para tornar todo o processo mais eficiente, ágil e dinâmico, estas abordagens costumam dar um ótimo resultado: - Use ferramentas de automação de feature engineering (como FeatureTools) para gerar um conjunto inicial de variáveis (features). Isso pode acelerar significativamente a criação das variáveis iniciais. - Crie dashboards que mostrem o impacto das mudanças de variáveis em tempo real. Isso permite ajustar rapidamente com base nos resultados e feedback das partes interessadas.

In my view, prioritise quick results for stakeholders by identifying key features that significantly impact model performance based on initial analysis or domain knowledge. Utilise automated feature selection tools like FeatureTools, AutoML libraries (e.g., H2O.ai, TPOT), or built-in scikit-learn methods to quickly identify and create relevant features. Adopt an iterative approach, implementing and testing features in rapid cycles using environments like Jupyter notebooks to demonstrate progress. Maintain regular communication with stakeholders, involving them in prioritisation to ensure alignment with business goals. Leverage cloud computing resources and parallel processing to accelerate feature engineering tasks efficiently.

Some keys are critical in this area, initial conditions ، process and final results. Then add time factor to this phenomena. When you talk about these terms and show the relationship between then , you can create good sense and confidence for others. It's so important that you know their language and vice versa. In clear atmosphere, we can see better and faster progress.

Sou muito fã de experimentação, e se você têm resultados práticos que apontam uma direção ou outra, são necessários argumentos muito fortes e embasados para invalidar estes resultados: - Em vez de tentar criar a solução perfeita desde o início, crie protótipos rápidos para testar hipóteses. Isso permite obter feedback inicial e ajustar rapidamente antes de investir muito tempo. - Minimal Viable Product (MVP) de features: Desenvolva versões mínimas viáveis das novas features. Entregue funcionalidades básicas que resolvam partes do problema e que possam ser incrementadas iterativamente com base no feedback.

Here is what else to consider: - Inverse feature engineering: Start with the desired outcome and work backwards Identify what features would be needed to achieve that result, then assess feasibility - Crowdsourced feature ideas: Set up a platform for employees or even customers to suggest and vote on feature ideas (Be careful with this option though to protect company private information). This can uncover unique perspectives and prioritize based on collective intelligence - Extreme simplification challenge: Challenge the team to explain each complex feature to a child. Use these simplified explanations to guide quicker, more intuitive feature development (Please read up on the Richard Feynman Technique)