To improve a disappointing predictive model: • Review data quality: Ensure the dataset is clean, complete, and representative of the problem. • Reassess features: Identify and incorporate new, relevant variables while eliminating noise. • Refine the model: Experiment with alternative algorithms, hyperparameter tuning, or ensemble methods. • Increase training data: Expand the dataset through additional collection or augmentation. • Validate assumptions: Test whether the underlying assumptions align with real-world scenarios. • Monitor feedback: Continuously evaluate model outputs against actual results to make iterative improvements. • Engage experts: Seek domain-specific insights to guide refinements.

your predictive model’s accuracy is underwhelming, start by revisiting the data. Check for issues such as insufficient volume, poor quality, or unbalanced classes, as these can significantly impact performance. Next, refine your feature selection by identifying and removing irrelevant variables while engineering new ones that might better represent the problem. Experiment with different algorithms or fine-tune hyperparameters to optimize the model’s performance. Validate the model with cross-validation techniques to ensure its reliability across various data subsets. Lastly, seek feedback from domain experts to ensure the model aligns with real-world expectations and continuously monitor its performance for iterative improvements.

When your predictive model underperforms, it’s a sign that something isn’t working as expected. This is the moment to step back and reassess your approach. It could be an issue with the data quality, the features used, or even the model itself. Refining your approach might involve cleaning or expanding the data, trying different algorithms, or adjusting parameters. It’s a process of trial and error, but each iteration brings you closer to improving accuracy and making better predictions.

By increasing the number of sample and using reliable questionnaires you will gain the targets in predictions. On the other hand using the true method for predictions is necessity of calculation for example i prefer multivariate analysing instead regression method

One strategy which I have found helpful is to do an error analysis, for example - I would look at the examples where the model's prediction was incorrect. I would look for patterns to identify if there are any data quality / labeling issues, or potential to come up with new derived features to help the model learn the difficult decision boundary. The error analysis is often missed by practitioners as majority focus on different ensemble models or sampling techniques.

Predictive AI runs on Data and for Predictive AI to have high performance quality of Data fed to the AI in the learning process is highly important. In order to get the quality data sets the 'Data Noise' is something that one needs to get rid of i.e., getting rid of data points that are unnecessary and meaningless which cause potential 'confusion' for the AI. Another useful technique could be Data Normalisation.

For accuracy we should value the each and every idea which is coming from anyone in the team instead of using the hierarchy and we should focusing on the product and the services which we are providing. We should be able to listen and willingly accept each and every idea from anyone which is coming

Sometimes in certain business context even lesser prediction power may not be a concern as long as you are able to draw insights and take business decisions keeping in view of cost of data itself. Else you may have to review the data itself used in predictive modeling. Also not to forget the context and external factors which may determine relevance of the data being collected. But given everything else remains same, we may have to address the quality of data or the modeling methods and techniques.

Improving the accuracy of a predictive model involves diagnosing where it falls short and implementing targeted strategies. If you liken the process to “timing the tide,” it suggests you’re seeking precision and alignment with external factors. Here are key approaches to improve your model’s performance: 1. Evaluate the Data: The Foundation 2. Model Diagnostics: Pinpoint Weaknesses 3. Refine the model architecture 4. Training Enhancements 5. Integrate External Knowledge 6. Post-Training: Continuous Improvement

The model is as good as what you feed in as data. Check for any outliers skewing the predictions and cull them out. Check all the predictions and formula used in the predictive model to ensure all are in place. Try to do a parallel run with your existing manual model or broader assumption based approach and test the model accuracy and reliability. This should have been done at the first place. If not it’s never too late. Also a model can only predict based on historical data and broader macro economic variables that we fed in. It cannot be very close to the reality. Keep a threshold for variables that we can’t control. If none of the above works we might have to go back to the drawing board.