What are the best ways to present regression analysis results with Python to stakeholders?

Regression is not only limited to "Linear Regression" or "Multiple Regression". There are others such as regression trees. Logistics Regression is not part of the regression family. It is a classification algorithm with a binary target variable. Let's keep it out of picture for now. Choosing the right regression method depends on various factors: - Parametric algorithms have assumptions like normality, homoscedasticity, linearity, multicollinearity, etc. Depending on these assumptions parametric or non-parametric tree-based regression methods can be used. - How do the users want to interpret the results: with linear regression sensitivity analysis is super simple. However, in tree-based regression, the rule set is easy to determine.

The models are based on certain assumptions. If assumptions of a model aligns with the business environment, then the model most probably is a good fit for that specific business environment. Same is true for regression models. For example if assumption of linearity and normal distribution hold true we can think of using linear regression. Now if there is Poisson distribution or Negative binomial distribution then Poisson or negative binomial regression will hold true. You can check the distributions of these 2, however the point is that selection of model should be based on alignment of model assumptions with business environment.

We need to list out key metrics used for the analysis, such as R Square. For technical audience it is important to show that all regression assumptions are satisfied and if there are any violations, why there are not a concern. For business audience it is important to provide business justification on why some variable is more important than others rather than basing results only on technical order. Overall showing back testing , out of sample analysis , sensitivity analysis, benchmarking complete the presentations.

Before presenting regression analysis results, first, determine your stakeholders. If my audience consists of data science students, I use bias-variance trade-off analysis to explain the deployment time effectiveness of a regression model. Depending on further interests, I employ bounds (upper and lower) to explain the failure modes of regression models. However, if the audience comprises c-suite executives or customers, I explain regression results using true value proposition methods. I use the following in my explanation: Training method and training data (transparency) Deployment time reliability Explanation of failure modes Tradeoffs between speed and accuracy Benchmarking results

The answer depends on the type of audience. For a highly technical audience, the emphasis should be on how the analysis was done including - Feature Engineering, Hyperparameter Selection, Cross Validation Results, performance in-sample and Out-of-Sample. For a non-technical audience, the emphasis should be on what was the key takeaways from the regression. For example, which features are coming most important. Also, for non-technical audience, it is also important to think from the business perspective and make the story along those lines.

When using Python for regression analysis, understand the business context, communicate results clearly, and validate predictions against real-world data. Leverage Python's visualization tools to enhance communication and gain deeper insights. Regularly update the model with new data and explore advanced techniques, such as feature engineering or hyperparameter tuning, for continuous improvement. We should be cautious of assumptions' impact on results. Address overfitting concerns by using regularization techniques available in Python libraries. Recognise that correlation doesn't imply causation and consider potential data biases. Handle outliers & maintain data quality vigilance throughout the analysis. Check for model accuracy regularly.

I have found the best tool for communicating the results of regression analysis, for most cases, is a slide deck. I agree with the article that it typically makes sense to generate the charts in Python. But presenting to stakeholders right out of a Jupyter Notebook often isn’t the best experience. My clients have really liked the slide decks we prepped for them to break down the complexities of the regression.

When you're showing off your ML model to the folks who call the shots, visuals are your best friend. Charts, graphs, you name it – they turn all that tech-heavy data into something everyone can get. It's about making the patterns, the ups and downs, and all the nitty-gritty clear as day. Plus, when you throw in interactive dashboards, you're not just talking at them; you're letting them dive in and see things move and change. This way, everyone gets the big picture, making those big decisions a whole lot easier. It's about cutting through the tech talk and making your model's story shine.

Visualization will not only help stakeholders but also you for understanding your data and distribution it follows. It also helps in validation of the model for example for linear regression model we can validate it by ploting the deviation between actual and prediction value.

Seconding Barbara that you will want to have the visuals presented in a way that’s easy for them to use. For a one time report this can be images from your IDE moved into a report or elsewhere. If it’s something that has dynamic or interactive features that business end users may want to play with, then a few common tools include power bi, tableau, or looker/data studio. A dashboard route can be great if those particular metrics are reviewed on a recurring basis. For Python there are also options like the streamlit library to move your model into a site that the end users are seeing it in an interactive but simplified manner

We have used statistics to provide quantification of business outcomes and impact. For example in a revenue forecasting application, if we can show quantitatively that increase in 1 USD worth of sales incentive spend, results in 1.5 USD of revenue increase, that will make sense to them. This can be observed through coefficients. However we have to test the stability of the model, before showing these results. Else if the impact changes significantly, then we will need to explain the change in impact.

In data analysis, stakeholders care more about practical significance than just statistical significance. It's about understanding meaningful connections in the real world. So, alongside statistical significance, we must focus on the effect size, conveyed by coefficients, to explain real-world implications. For instance, a statistically significant coefficient with a small effect size might have negligible real-world impact. Also, we should delve into the significance of different features and how they collectively influence the outcome. This not only broadens the scope of the analysis but also taps into the stakeholders' expertise—after all, they know the features better! This might uncover untapped intricacies of the data.

When presenting regression analysis results, it's crucial to interpret coefficients and metrics in a clear and meaningful manner. Explain the significance of each coefficient, detailing its impact on the outcome variable and the confidence level associated with its estimate. Report performance metrics like R-squared, adjusted R-squared, p-values, or mean squared error, elucidating their implications for model accuracy. Avoid technical jargon, opting for plain language and concrete examples. For instance, articulate that a one-unit increase in variable X corresponds to an average increase of Z units in the outcome variable Y, holding all other variables constant.

1. Begin by providing a concise summary of the regression model's purpose and context. 2. Present the coefficients with their corresponding variable names, emphasizing their impact on the dependent variable. 3. Include relevant metrics such as R-squared and p-values to assess model fit and significance. 4. Interpret coefficients in real-world terms, considering the units of variables for practical understanding. 5. Conclude with actionable insights, highlighting key factors influencing the dependent variable based on the analysis.

Interpreting regression outcomes for stakeholders involves simplifying statistical intricacies. Coefficients clarify variable impacts, indicating the change associated with a one-unit increase in predictors. Metrics like R-squared offer a holistic model goodness of fit view, while p-values express statistical significance. Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) provide insights into predictive accuracy. Confidence intervals define likely variable impacts, aiding precision understanding. Stakeholders grasp the model's reliability, variable significance, and overall predictive strength, facilitating informed decision-making aligned with their goals.

We primarily use performance metric to compare multiple models. We select that model which provide higher performance consistently. Also comparison of performance across models are easier to explain to stakeholders than explaining statistics like goodness of fit etc. For myself and my team, the statistics are more for data science team to be sure that model is stable, is not under or over fitting etc.

When navigating the complex landscape of model selection, Python offers a robust toolkit for effortless comparison. Utilizing sklearn, you can seamlessly evaluate diverse models by tweaking variables, transformations, and interactions. Split your data wisely with sklearn's train-test split, employing cross-validation to gauge model generalizability. For a deeper analysis, leverage statsmodels to conduct hypothesis tests or ANOVA, unveiling the significance of distinct models. Present your findings with clarity, guiding your audience through the intricacies of model nuances. In this journey, Python becomes your compass, guiding you toward the model that best unravels the story hidden within your data.

Comparing & contrasting different models involves using evaluation metrics such as R-squared, MSE, and RMSE, typically with libraries like Scikit-learn and statsmodel in python. Implement cross-validation for robust performance assessment and analyze residuals for pattern detection.Evaluate feature importance scores to gauge variable significance. Balance between model complexity & interpretability,testing assumptions, and exploring regularisation techniques like Ridge & Lasso. Assess computational efficiency, particularly for large datasets. Validate domain-specific considerations and leverage ensemble methods like Random Forest for enhanced predictive capability. Regularly update & compare models to ensure ongoing monitoring & performance

When faced with the need to identify the most suitable model for analysis, utilizing Python facilitates the creation and evaluation of multiple models with diverse variable combinations, transformations, or interactions. The sklearn library proves valuable for splitting data into training and testing sets, employing cross-validation, or other techniques to gauge model generalizability. Additionally, the statsmodels library allows for hypothesis tests or ANOVA to compare model significance. Present the comparison results clearly, concisely explaining the rationale behind selecting a particular model over others.

Some people prefer a boosters in their machine learning models as a way to overcome shortcomings of traditional train/test splits

Conclude your regression analysis by offering clear recommendations and acknowledging limitations. Highlight key findings and implications for stakeholders, suggesting actionable steps based on the results. Explicitly address the limitations and assumptions, discussing potential sources of error or bias. Maintain transparency and honesty, avoiding unsupported claims. Consider outlining future directions or improvements, such as collecting more data, incorporating additional variables, or exploring alternative methods. This comprehensive approach ensures stakeholders are well-informed about the analysis's insights, its constraints, and potential avenues for refinement.

Linear reg models assume linearity, independence of errors, homoscedasticity, and normality of residuals. Violations of these assumptions can affect the model's accuracy. Outliers can significantly influence regression models, potentially leading to biased parameter estimates. Identify and handle outliers appropriately. Complex models might overfit the training data, capturing noise instead of true patterns. High correlation among predictor variables can lead to multicollinearity issues, making it challenging to isolate the individual effects of predictors. Poor-quality data, with missing values or inaccuracies, can negatively impact the performance of regression models. Address data quality issues appropriately.

It is important to understand whether the regression model is really stable in relation to the available number of cases. In other words, is the number of cases sufficient to apply the regression to the data. In his book "Regression Modelling Strategies", Frank E. Harrell provides helpful rules of thumb on how many observations, or events, are needed in relation to the number of independent variables for different types of regression in order to obtain a stable model. This is particularly helpful for observational studies where you have no influence on the number of cases. For example, in a logical regression, you need around 10 to 20 expressions or events of the dependent variable for each independent variable in the model.

Have a honest and open communication highlighting the limits of the study: this will prevent questions on the biases and indeed can open the doors to further development!

In addition to the technical skills and points mentioned above, one needs to develop presentation / writing / communication skills in order to effectively and efficiently relay the matter to any audience! Without this piece in place, even the most meticulously executed technical work can be misunderstood by its consumers.

Beyond technical prowess, effective regression modeling demands domain expertise for contextual alignment. Precisely defining objectives, ensuring data quality through preprocessing, and engaging in feature engineering contributes to accurate model outcomes. Communication skills play a pivotal role in articulating complex results to diverse stakeholders. Collaboration with domain experts, ethical considerations in handling sensitive data, and interpersonal skills enhance the modeling process. Rigorous evaluation, prioritizing interpretability, and a commitment to continuous learning for staying abreast of advancements contribute to the overall effectiveness of regression modeling in practical applications.

You must speak the language of your audience. If its C-Suite, show the benefit of the new model/modelling approach. Whether it's an uplift in Sales, Revenue or Profit. If your audience is below C-Suite, then much of the same, but you can get away with explaining the effort and benchmark the "sophistication" of the model/modelling approach etc and effort spent. If its peers and first line management then you can go the whole 9 yards and present the mode or even the code in action if you want. Intrigue amongst peers is crucial for future improvements. Just consider the language and motivations of your audience.

○ Start with a Strong Hook: Introduce the problem and its significance to engage your audience. ○Methodology Overview: Explain your approach in simple terms and avoid deep technical details. ○ Visual Presentation of Results: Use charts and graphs, like those from Matplotlib, to make your findings clear and engaging. ○ Contextual Comparison: If relevant, compare your results with industry standards or other companies. ○ Translate Findings to Real-world Implications: Discuss the practical implications of your results in terms of key metrics or business goals. ○ Offer clear, actionable recommendations based on your analysis. ○ Encouraging audience interaction and feedback ○ Including a detailed technical appendix for in-depth information.

Moreover, when the model is integrated into products, automatic monitoring of model shifts is essential. In many instances, these shifts arise due to changes in the data. Consequently, it becomes imperative to gather fresh data and potentially update or reconstruct the model periodically.