What is the best way to validate your data model after cleaning?

In my experience, effective data validation involves a crucial step: splitting the dataset into training and testing sets. For instance, in a housing price prediction model, training on historical data and testing on unseen data assesses the model's predictive prowess beyond the training set. This strategy helps unveil potential overfitting issues, ensuring the model's reliability in making accurate predictions on novel data, a key aspect of real-world applicability.

Splitting your data into training, validation, and testing sets is an initial step in an AI-driven healthcare project because it allows you to train your model on a subset of the data, evaluate its performance on a separate set, and finally test it on unseen data. By doing so, you can assess the model's ability to generalize and make accurate predictions on new examples. After cleaning the data, it's necessary to validate the data model to ensure that the cleaning process didn't introduce any biases or distortions that could impact its performance. Validation helps to verify the reliability and effectiveness of the model, providing confidence in its ability to make informed decisions and assist in healthcare tasks.

One thing I have found useful was using two methods of splitting the data, which are: 1. Training/testing set: Split your data into two sets: training (used to fit the model) and testing (used to evaluate how well the model generalizes to unseen data) 2. Cross-validation: Divide your data into k folds, train on k-1 folds, and test on the remaining folds. Repeat this process for all k folds and average the results for a more robust evaluation.

The practice of splitting data into training, test, and validation sets is not just a procedural step; it's a strategic move to enhance the reliability of your data model. It introduces a checks-and-balances system, ensuring your model is robust and not excessively tailored to the training data. This approach mirrors the real-world scenario where models encounter new, unseen data. It's a preventive measure against overfitting or underfitting, reflecting a commitment to creating models that generalize well and maintain predictive accuracy across diverse datasets.

In my experience, a fundamental approach to validating a data model post-cleaning involves the strategic division into training and test sets. The training set is pivotal for model fitting, while the test set evaluates predictive accuracy on new or unseen data. Introducing a third subset, a validation set, proves beneficial for refining model parameters or selecting the optimal model from various options. This approach is instrumental in preventing overfitting or underfitting, striking a balance that allows the model to capture meaningful patterns without succumbing to noise or missing vital data nuances.

Cross-validation helps assess how well the model generalizes to unseen data by splitting the available data into multiple subsets, training the model on some and testing on the others. This process helps detect potential issues such as overfitting or underfitting, enabling researchers to fine-tune the model and improve its performance, which is vital and very vital in healthcare to ensure reliable and trustworthy predictions.

Employing cross-validation goes beyond a single snapshot evaluation; it's a dynamic strategy providing a nuanced understanding of your data model's performance. By systematically cycling through different subsets as both training and test data, it mitigates the risk of model evaluation being influenced by a specific partition. This approach not only offers a more reliable estimate of performance but also unveils insights into how the model generalizes across diverse scenarios. In essence, cross-validation becomes a robust quality control mechanism, minimizing biases, reducing variance, and safeguarding against inadvertent data contamination, contributing to the overall trustworthiness of your data model.

In my experience, another valuable approach to validating a data model post-cleaning is employing cross-validation. This involves dividing the data into k folds, utilizing each fold as a test set iteratively, and the remainder as a training set. By testing the model on different subsets and averaging results, cross-validation offers a more robust estimate of performance. This method proves effective in reducing variance and bias, guarding against data leakage, and ensuring a reliable evaluation of the data model. It's particularly beneficial for preventing unintentional contamination of the training process with test data.

Cross-Validation and Leave-One-Out Cross-Validation: These techniques involve splitting the data into multiple folds, with each fold used for training and the remaining fold for testing. This process is repeated, and the results are averaged to obtain a more accurate estimate of the model's performance.

Cross validation of the data is a must for any data/ features to be declared as efficient for model development. Setting a good CV scheme is as important as building a good data pipeline. For starters, I recommend using a simple single model apropos to the assignment. Once the feature set is finalised after a number of repeated iterations of data engineering- model creation, one can fuse and ensemble diverse models to develop a stable and robust ensemble.

In my experience, a third effective avenue for validating a data model post-cleaning involves the utilization of metrics and plots to gauge its alignment with data and objectives. Depending on the model's type and purpose, employing diverse metrics and plots like accuracy, precision, recall, F1-score, ROC curve, confusion matrix, residual plot, R-squared, MSE, MAE, and more can provide valuable insights. These tools not only assess the model's quality, accuracy, sensitivity, specificity, and error but also highlight potential problems or limitations. Incorporating these metrics and plots ensures a thorough and nuanced evaluation of the data model's performance.

Employing a diverse set of metrics and plots for model validation isn't just about numbers and visuals; it's a strategic lens offering a multifaceted view of your model's performance. Each metric and plot serves as a diagnostic tool, revealing specific aspects of your model's behavior and effectiveness. Beyond a simple accuracy score, these measures provide nuanced insights into precision, recall, and other critical parameters, aligning your evaluation with the intricacies of your objectives. This comprehensive approach doesn't just assess performance; it unveils potential pitfalls and limitations, guiding you to refine and optimize your data model for real-world effectiveness.

By using metrics such as accuracy, precision, recall, and F1-score, researchers can quantitatively evaluate the performance of their models. I think, these metrics provide insights into how well the model is predicting outcomes based on the cleaned data. Plots like confusion matrices and receiver operating characteristic (ROC) curves help visualize and interpret the model's behavior. These graphical representations allow researchers to identify areas of improvement and make necessary adjustments to enhance the model's accuracy and reliability in healthcare applications.

Performance metrics vary by model goal: 1. Binary Classification: Accuracy, Precision, Recall, F1, ROC-AUC. 2. Imbalanced Classification: Precision, Recall, F1, PR AUC. Note: Accuracy less informative; focus on TN, FN for minority class. 3. Regression: MSE, MAE, R-squared. 4. Multi-Class Classification: Accuracy, Precision, Recall, F1, Confusion Matrix. Confusion matrix aids class-specific assessment. 5. Visualization: Use plots for better interpretability. Example plots: ROC, Precision-Recall curve.

Accuracy : when data is balanced, it will give good clarity on model performance. It will give more importance to only one class. F1score: when data is imbalanced and if both classes performance is equally important. F1score is a good choice. Precision: It will measure predicted positive class performance. Recall: It will measure actual positive class performance. ROC AUC: cure nearer the true positive rate indicates best model MSE: squared error creates more bias to negative values MAE: Absolute error gives best clarity on model and mitigates the squared bias. L1 vs L2: L1 is used to save from model overfitting while L2 is used to save from model underfitting.

Comparing data with benchmarks is valuable in an AI-driven healthcare project as it allows for meaningful evaluation and assessment of the performance of the data model. By comparing the model's results against established benchmarks, such as industry standards or expert opinions, potential biases, errors, or inaccuracies can be identified and addressed. This validation process ensures the reliability and accuracy of the data model, thus enhancing the overall quality and credibility of the project's outcomes.

In my experience, one key aspect of successful data model validation post-cleaning involves a thorough analysis using diverse metrics and plots. Utilizing tools like accuracy, precision, recall, F1-score, ROC curves, and confusion matrices provides a comprehensive evaluation of the model's quality, highlighting its strengths and potential limitations. These metrics not only gauge performance but also offer valuable insights into sensitivity, specificity, and overall alignment with the intended objectives. Incorporating such a multifaceted assessment ensures a robust validation process, contributing to the model's effectiveness and reliability.

Pros: Benchmarking against industry standards or previous models can provide context for the model's performance, setting a baseline for improvements. Cons: Benchmarks may not always be representative or up-to-date with the current state of the art or specific use cases.

The best way is by removing the outliers & by ploting charts. Remove deviations and then run the models, starting from the base model to advance model. Compare the results. Always remember to adjust standard errors and finally execute the post (estimation) analysis tests for the model validity.

Compare whether the data in the train and test sets are comparable or not. If not, it might be difficult to train a model that performs well on both train and test data. Even when the performance in cross-validation is good on the training data, we might have a suboptimal model on the test data.

I guess testing with the real world is more appropriate as the model would interact with newer data and we can check and validate on different real time scenarios which would be more appropriate to our buisness objectives

Subjecting your data model to real-world scenarios is not just a formality; it's a litmus test for its practical viability. Real-world data often introduces complexities like noise, outliers, and variations that challenge the model's adaptability. Testing with diverse sources and formats mirrors the unpredictable nature of actual applications, providing insights into the model's robustness and scalability. This pragmatic validation isn't merely about performance metrics; it's a pragmatic exercise that ensures your data model aligns with the dynamic and unpredictable nature of the environments it's designed to navigate, reinforcing its reliability and relevance in practical settings.

Testing with real-world data is crucial in an AI-driven healthcare project as it allows for the evaluation and validation of the data model after the cleaning process. By incorporating real-world data, researchers and developers can assess the performance and effectiveness of the model in a practical setting, ensuring its reliability and capability to handle diverse and complex scenarios. This validation step is necessary to gain confidence in the accuracy and generalizability of the data model, ultimately contributing to improved decision-making and patient outcomes in healthcare.

En mi experiencia este paso es fundamental antes de llevar un modelo a producción, siempre probar con datos reales, por qué allí se podrán ver desviaciones o sesgos del modelo, así como también su estabilidad versus outliers, inclusive de los resultados de este paso convenga regresar y hacer ajustes en el modelado.

Using real-world data when validating data models is a very essential step in ensuring the scalability of the model. Real-world data helps provide better insights into how well the model scales and its generalization capabilities to new/unseen data. It also helps to assess how well the model adapts to handling noise, outliers, and varying data formats.

Experts have domain knowledge and experience in the field, allowing them to identify potential biases, errors, or limitations in the data model. Their insights can help ensure the accuracy, reliability, and effectiveness of the model, improving its ability to make informed healthcare decisions and ultimately benefiting patients and healthcare providers.

Think of learning to cook – you taste your own food, but getting a chef's opinion is invaluable. In data modeling, experts act as the seasoned chefs. They bring years of experience to the table and can provide insights that might not be obvious to someone just starting. Their feedback is like adding a pinch of expertise to make your model more refined and effective.

in my experience, usually you will need to have an evaluation strategy to assess the expert's contribution.For example each prediction can be assessed by three different experts, as they might not agree with each other.

In my experience, getting feedback is also so important. It is so hard to know everything today and Data Science is a diverse area. You can be a great programmer but you also need to be a great domain expert. So, it is normal we have some backwards in some conditions and asking to someone who is better in that area is a useful way. I always try to share experience and knowledge with people who I think they know it. This is something that it needs to be.

Obtener comentarios de expertos y sobre todo expertos con "trabajo en campo" es vital para el uso del modelo en ambientes reales de negocio, ayuda a tener insights de lo que sucede en el mundo real y habilita conversaciones para enriquecer el modelo así como también abonar el terreno para la inclusión y gestión del cambio.

Generally, models may be validated by two approaches: 1- External or extrinsic approach: comparing the resulting output of the model to an external real entity or model data. 2- Internal or intrinsic: validate the model according to its well-behavedness in known situations. Sensitivity analyses and gradient analyses are examples of this approach.

After performing data cleaning in an AI-driven healthcare project, it is essential to consider factors to validate the data model effectively. It is necessary to evaluate the accuracy and consistency of the cleaned data by comparing it with ground truth or expert-labeled data. Then, assessing the model's performance through appropriate metrics, such as precision, recall, and F1 scores, can help determine its effectiveness. Conducting cross-validation or utilizing an independent test dataset can provide generalizability assessment and help identify potential overfitting issues. Incorporating domain expertise and involving medical professionals in the validation process can ensure the model aligns with clinical requirements and standards.

In my experience with Econometric Modelling, I have understood. More than the data science concepts and application, understanding data and what it unrolls is of higher importance as it helps the researcher to play with the data in multitude of ways and derive insights from it.

We should not only be suspicious when the model performs poorly. Often when a model performs too well, there is something wrong with the model.