How can you balance creativity and accuracy in predictive model development?

Understanding the problem is the most important thing in developing solutions. Most of the time we see founders so determined in addressing a problem that only them think that needs a solution! The truth is that collecting as many feedback as possible is what will help them understand what really the market needs! That’s the way of building a model that is really appealing and addressing the problem in the right way! Ask as much as you can, you’ll be surprised of the feedback you’ll get 🧡

Here are some strategies in my own opinion to achieve this balance: 1. Creativity in model development starts with a deep understanding of the business problem. 2. Use EDA to creatively explore the data. 3. Think about how different data elements can be combined or transformed to better capture the nuances of your problem. 4. Experiment with a variety of algorithms, including newer or less conventional ones, to see which offers the best balance of accuracy and insight. 5. To ensure accuracy, use techniques like cross-validation to avoid overfitting. 6. Start with a simple model, evaluate its performance, and then gradually increase complexity.

Si dedicas suficiente tiempo a encontrar el verdadero problema y a plantear un buena hipótesis tienes más del 70% de problema resuelto , con un pensamiento crítico bien desarrollado y sustentado, poco a poco será más sencillo plantear y definir el problema , evitar re trabajos o sesgos

Developing a predictive model is a dance between creativity and accuracy. Innovate in problem-solving, then ground your ideas in rigorous data validation. It's a delicate balance between thinking outside the box and ensuring your model accurately reflects real-world scenarios.

The idea of understanding the problem is a basic requirement of any investigative process. It will be very challenging to move forward if you don't understand what you are up to. Thus, the next step would be the wrong method and inappropriate data. And the product will be overfitting, underfitting, and a biased model.

Incorporate systems thinking to understand complex interdependencies within the problem domain. This holistic view can help in identifying less obvious factors that could significantly impact the predictive model's accuracy.

Building effective predictive models requires a delicate balance between two seemingly opposing forces: creativity and accuracy. While creativity sparks innovation and exploration of new ideas, accuracy ensures the model's reliability and usefulness in real-world applications. Define the problem: • Clearly state the target variable you want to predict and the context of the problem. • Identify the business objectives and stakeholders involved. • Set specific and measurable goals for the model's performance.

Compare Predictive Model Performance Against Random Results With Lift Charts and Decile Tables. Evaluate the Validity of Your Discovery With Target Shuffling. Test Predictive Model Consistency With Bootstrap Sampling

Data Exploration Methods: 1. Statistical analysis: • Calculate descriptive statistics. • Perform exploratory data analysis (EDA. • Hypotheses testing 2. Visualization: • Create various charts and graphs. • Interactive visualizations 3. Dimensionality reduction 4. Feature engineering: • Create new features. • Transform existing features. 5. Data cleaning and pre-processing: • Handle missing values • Deal with outliers • Clean and transform data. Tools: • Python libraries • Platforms like Jupyter Notebook and Google Colab. • Commercial tools: Tableau & Power BI Start with simple analyses and gradually increase complexity.Document your findings and observations.Stay curious and ask questions about your data.Iterate & refine your exploration

Data exploration is pivotal in predictive models. Utilizing statistics, visualizations, and engineering techniques unveils patterns and manages missing values or anomalies. This enhances data understanding, fosters creativity by uncovering new opportunities, and ensures greater accuracy in model construction.

Data are keys, so start by pinpointing exactly what you're solving. Gather data from as many sources as possible, then clean it up – think of it like prepping a dinner for your wife (or husband). Dive into Exploratory Data Analysis (EDA), meaning that you go to spot trends and oddities. Now that you have patterns model them. Interpret your findings with a mix of Sherlock's insight and don’t forget common sense. P.S. Always double-check your results. You’re almost there, now you need to present what you found in a way that even your grandma would get. Finally you’re ready, put those insights into action. Don’t forget that data are cool when you know how to play it right! The world is yours, take it 🚀

Utilize advanced analytics tools like Principal Component Analysis (PCA) to identify the most relevant features in your dataset. This method helps in reducing dimensionality, focusing on essential data and enhancing both creativity and accuracy in model development.

A mi me ha resultado utilizar herramientas de análisis avanzados para separar el ruido y datos atípicos de las bases y así poder centrarme en formas creativas de presentación y visualización de datos que a su vez permiten detectar oportunidades tanto de mi modelo como de la data analizada

Choosing the right model depends on your data type and problem. For binary outcomes, logistic regression works well; for complex patterns, try decision trees or random forests. Use PCA for data with many variables. Time series? ARIMA is your way to go. If standard models don't fit, consider custom models. Don’t forget the most exciting part when dealing with data: experimentation is key.

Experiment with hybrid modeling approaches, combining traditional statistical methods with advanced machine learning algorithms. This blend can leverage the strengths of both approaches, potentially leading to more robust predictive models.

Validating a model? Here's how it's done. First, slice your data into two – a training set for the model workout, and a testing set to really put it through its paces. Train it, test it, and watch metrics like accuracy or precision. But don't stop there. Use cross-validation and check the model’s adaptability. The world changes, data changes as well, so keep feeding fresh data. This isn't just validation; it's giving your model a reality check 🙏🏼

Implement a 'model ensemble' technique where multiple models are used and their predictions are combined. This approach can often result in better performance compared to individual models.

Develop interactive tools or dashboards that allow users to manipulate variables and observe the effects on model predictions. This can aid in understanding the model's dynamics and provide valuable insights into how different factors influence outcomes.

Interpretation can give space for creativity and imagination. Even more, it’s the right place to add those components because without them models would be less competitive.

Establish a feedback loop with end-users of the model. Direct user input can provide practical insights that can be used to refine the model in subsequent iterations.

Un paso importante para retroalimenta y mantener actualizado tu modelo es el probarlo constantemente y asegurarte de que sigue vigente , si perdemos de vista los resultados o cambios en la información este puede quedar obsoleto sin darnos cuenta

Your model creation, in fact all facets of machine learning lives and dies on the quality of the data. It's no surprise that the concept of a Feature Catalog was picked up by Databricks to solve the very problem of data quality in ML. The challenge is that data good enough for the best BI report just isn't good enough for ML. Nulls values, bias, poor distribution, and outliers must all be dealt with. So, before you embark in building a predictive model, make sure that your data is the best it can ever be. No ML algorithm can fix what is already broken. Junk in, will always lead to junk out

Reflect on the ethical implications of predictive modeling. Consider how model predictions might affect different groups and ensure that the model does not reinforce biases or unfairness. Ethical considerations are crucial in maintaining trust and integrity in predictive modeling.

There is no balancing between creativity and accuracy in my opinion. To me, both are complementary to each other and mandatory for a good solution. Accuracy (I am reading it as ‘the relevant performance metric of the machine learning model’) does depend upon quality of all the aspects from technical problem formulation, data, solution approach, algorithms, tuning, model maintenance and there is creativity in all especially in problem formulation, data quality assessment, how to develop solution in less resources (time, effort and cost) and most critically in articulating the limitations of the solution to the customer.