What are the common mistakes to avoid during model validation?

When building machine learning models it’s extremely important to define the problem that you seek to solve with these algorithms once you know your target you can easily identify the effectiveness of the algorithm towards achieving this target in another words you can select which metrics are relevant to validate the performance of your model , we as data scientists spend much time in processing the data and building algorithms but too little is spent in measuring the performance of our models , different situations require different set of metrics to validate the ML models e.g to build fraud detection model requires different set of metrics than building an image classifier so enough time must be given to avoid wrong conclusions

Choosing the right metric is similar to setting the compass for a journey. I've seen many projects veer off course simply because they measured success with an ill-suited metric. A practical example is in medical diagnostics. If a rare disease affects 1% of the population, a model that always predicts "no disease" will still be 99% accurate, but it's utterly useless in practice. Always align your metric with the real-world implications of errors.

Choosing the right metric in model validation is crucial and context-dependent. In consulting, ROI-based metrics could outperform traditional stats like accuracy, as they tie directly to business outcomes. In media, engagement metrics like CTR go beyond mere 'accuracy' to measure true user interaction. For SaaS, custom metrics aligned with unique business goals might be more telling than off-the-shelf ones. Remember, even the most statistically sound metric can be flawed if it results in ethical bias. Your choice of metric should be a strategic decision, not an afterthought.

In my openion understanding the context and objectives is most essential when selecting the appropriate metric ? to gauge an ML model's success. The choice of correct metric can profoundly impact model development and decision-making. In classification tasks, metrics like accuracy , precision ?, recall ??, and F1-score ?? are tailored to different aspects of performance, making them suitable for diverse scenarios. Meanwhile, regression tasks may require metrics like mean squared error (MSE) or mean absolute error (MAE) to quantify predictive accuracy. In healthcare, for example, sensitivity and specificity matter greatly for diagnostic models, while in finance , minimizing error might be the top priority.

I agree that it's crucial to use the appropriate metric for model validation. It's similar to picking the appropriate tool for the job. You won't obtain the greatest outcomes if you choose the incorrect tool.

Incorporating diverse datasets not only enhances model generalization but also provides a more comprehensive evaluation of its performance across various scenarios and conditions. Real-world complexities demand a multifaceted validation approach that goes beyond a singular dataset for a more robust and reliable model assessment.

Putting all your eggs in one basket? Not a good idea, especially with datasets. If you train and test your model on just one dataset, you might end up with a model that's too tailored to that specific data and flops with new data. It's like practicing for a quiz only using last year's questions. To get a model that's truly reliable, mix things up! Use different datasets to validate your model. Techniques like cross-validation or using external data sources can help. This ensures your model is versatile and doesn't get tripped up by unexpected data quirks.

Relying solely on one dataset for model validation can not only be misleading but also risky for any business that aims to make data-driven decisions. This is particularly true when working with marketing data platforms like HubSpot. While these platforms offer rich insights, they only give a partial view of the customer journey. To create a robust model, data from HubSpot should be cross-verified and enriched with information from other funnel systems. For example, finance teams may have data related to customer lifetime value or purchase frequency that could offer an additional layer of insight. Similarly, web analytics platforms like Google Analytics can provide invaluable data on user behaviour, conversion rates, and engagement metrics.

Relying on a single dataset for model validation is like judging a restaurant based on a single meal. Each dataset provides a narrow snapshot of reality, missing broader context. Models validated on a single source may overlook flaws that become evident with new data. Models can overfit to peculiarities of one dataset without capturing performance inconsistencies across different conditions and populations. For more reliable validation, it is best to test models on multiple independent datasets that represent diverse settings and use cases. A wider range of validation sources leads to models more robustly prepared to handle new situations encountered during real-world deployment.

Depending on a single dataset can lead to overfitting, where a model performs exceptionally well on that specific data but fails to generalize to new, unseen data. To mitigate this, it's crucial to split your data into training, validation, and test sets, and apply cross-validation techniques if applicable. This approach helps ensure that your model's performance is robust and representative across various data distributions, reducing the risk of making decisions based on an overly optimistic evaluation.

I can't stress how important it is to always know your assumptions throughout the whole model validation process. I once did a model validation, and everything worked perfectly fined. But when I tested the model using actual data, the whole model failed. I only realised that I made a huge assumption when I was training the model without even being aware of it. Moral of the story? Never ignore your assumptions.

There can be a situation where a classification model, built on the assumption of feature independence, struggles with correlated predictors. This oversight can result in biased predictions, emphasizing the importance of not only checking assumptions but also adapting the model when confronted with real-world complexities. Therefore it is important to acknowledge that assumptions are not rigid rules but rather guidelines subject to validation and refinement based on the intricacies of the data.

When building regression models, check for and eliminate multicollinearity by removing correlated predictors or combining them into one predictor. Otherwise, overfitting would occur, and the model performance ruined.

Ignoring the assumptions made while building the model is a common mistake during model validation. For example, if you build a linear regression model assuming a linear relationship between the features and the target variable, but the actual relationship is non-linear, then your model will not perform well. It's like using a hammer to drive a screw - you may be able to get the job done, but it won't be as effective as using the right tool for the job. To avoid this mistake, always check the assumptions (i.e. linearity, independence, homoscedasticity, normality) of your model and make sure that they are valid for your data. If they are not, then you need to either transform your data or use a different model.

Ignoring model assumptions is like driving with your eyes closed; it might seem fine until you hit something. Assumptions, whether about data distribution or relationships, are the foundation of model validity. Ignoring them risks unreliable predictions and calls for a reality check through thorough diagnostics and validation checks. It's not just about building models; it's about building models on solid ground.

Starting with model validation, always consider its end-use. I recall a firm whose model excelled in tests but faltered in real-world scenarios due to socio-economic factors not in their dataset. This exemplifies why technical accuracy isn't enough; understanding the broader application context is key. Similarly, teams might disagree on which validation metric best aligns with business goals. Such disputes underline the importance of grounding models in real-world context, ensuring their usability and appropriateness for intended audiences.

The model may perform differently in a production environment. so it is required to test it with real-world data to identify any unexpected issues.

In my experience, overlooking the real-world context can have tangible consequences. I once worked on a recommendation system for an e-commerce platform that excelled in technical validation but failed to account for user preferences in different geographical regions. The algorithm, optimized for a Western market, struggled to adapt to cultural nuances, leading to recommendations that were irrelevant or even offensive in certain regions. This underscored the importance of contextual validation, emphasizing the need to consider diverse user experiences and cultural sensitivities beyond conventional performance metrics.

1st, data leakage is a critical concern; it's essential to ensure that information from the validation or test set does not unintentionally influence the training data, as this can lead to overly optimistic performance estimates. 2nd overfitting to the validation set is a pitfall to watch out for; hyperparameter tuning based on validation performance can result in a model that is too specialized to the validation data. Instead, a separate test set should be used for final evaluation. Thirdly, ignoring class imbalance can be problematic, especially in classification tasks; relying solely on accuracy can be misleading. Metrics like precision, recall, or F1-score should be employed to account for class distribution.

Overlooking real-world application context is a frequent pitfall in model validation. I recall working with a team that developed an algorithm to identify high-risk patients based on medical records. The model excelled on standard accuracy metrics against test datasets. However, upon validating it with clinicians, flaws emerged - it overlooked socioeconomic factors influencing patient outcomes. Doctors found the algorithm unsuitable for guiding care in their underserved communities. This experience highlighted that technical performance in isolation means little without validating a model's context of use.

One mistake many often make is limiting model validation to local validation, which we do during training. Once you have deployed a model, we must periodically validate in production as well, i.e., when it is serving users. This is because, after deployment, many things could go wrong: - concept drift - covariate shift - feature nonstationarity, etc. Many often limit validation to local validation and hope things will continue to stay consistent as they were during development. But that is rarely the case. A few things I do to validate in deployment: - collect new data (if viable) - log prediction - get user feedback, etc. Next, I use this data as signals to determine the model's reliability, plan model updates, etc.

In addition to the points made, it's crucial to recognize that model validation should be an ongoing process, not just a one-time event. Real-world data is dynamic and can change over time. Therefore, regularly revisiting and revalidating your model in its actual context ensures that it continues to perform effectively and ethically as circumstances evolve. This iterative approach helps maintain the model's relevance and trustworthiness, aligning it with the ever-changing needs and expectations of stakeholders and users.

Some other things to consider apart from the ones mentioned above- Not Shuffling Data: Especially when data has some order-based structure, failing to shuffle it before splitting can lead to non-representative training and test sets. Single Validation Set: Relying on a single train-test split can be problematic. It's often better to use techniques like k-fold cross-validation to ensure that the model performs well on different portions of the data. Ignoring Model Assumptions: Some algorithms have specific assumptions about data (e.g., linear regression assumes linearity, homoscedasticity, etc.). Ignoring these can lead to invalid model conclusions.

A good Model Validation Methodology is essential for the success of Model. Apart from the above, I would recommend the segmented model evaluation. Instead of Evaluating the performance in Train Test and OOT sample, use the business channels. Identify key segments (Like Country/State wise or Origination channels, etc.). A model may perform well in one segment and not so well in other, hence talk to the stake holders and align. Perform frequent model monitoring. Check the inputs and model scores on timely basis. Track data drift/data distribution over time - use CSI and PSI. If the data is not matured yet (ground truths), then try to validate on short term targets. Update your Model if the model monitoring results indicates low performance.

I believe Model validation goes beyond assessing accuracy; it encompasses deployment, monitoring, updates, and ethical considerations. Deployment involves seamless integration into real-world systems. Monitoring ensures ongoing performance in changing environments, addressing issues like data drift. Ethical AI concerns demand fairness audits and transparency, guarding against bias and privacy breaches. Incorporating these aspects ensures AI models not only excel but also responsibly benefit society. A holistic approach is crucial for long-term success and trust in the ever-evolving world of data and AI. What do you feel?