What are the best ways to evaluate the accuracy of a predictive analytics model?

In my experience, defining goals and metrics is crucial for model relevance and effectiveness. A common pitfall is selecting metrics that don't align with business objectives. For instance, a model predicting customer churn with high accuracy might still be ineffective if it doesn't capture the nuances of customer behaviour relevant to the business. In my teaching, I emphasise the importance of understanding the business context and selecting metrics that reflect it. It’s also vital to recognise the trade-offs between different metrics. For example, optimising for precision may reduce recall, which could have significant business implications.

Evaluating the accuracy of a predictive analytics model involves various techniques to assess its performance. Here are some key methods: ? Mean Absolute Error (MAE) and Mean Squared Error (MSE): Calculate these metrics to measure the average difference between predicted values and actual values. ? Root Mean Squared Error (RMSE): Similar to MSE but provides a measure in the same unit as the target variable. ? R-squared (R2) and Adjusted R-squared: Evaluate the proportion of variance explained by the model. ? Accuracy, Precision, and Recall: For classification models, assess accuracy—the proportion of correctly predicted cases.

In my experience, I'll be crystal clear on what exactly I'm trying to predict. And define one or two simple metrics for judging the predictions against the actual outcomes. Like if I built a model to predict my weekly calories burned, I'd care most about percent error from my fitness tracker's readouts.

Goals may be many, metrics are evaluated via conversion, action to calls, or lead forms in many cases. Business may vary, but the potential information of investment made for online advertisement is always relied upon the materialization of an activity

In my opinon, to gauge predictive analytics accuracy, focus on metrics like Mean Absolute Error, Root Mean Squared Error, or precision-recall for classification models. Cross-validation and comparing against baseline models provide additional insights. Always validate performance on diverse datasets for robustness.

Splitting data into training and testing sets in predictive analytics is crucial for model validation. The training set teaches the model patterns in data, while the testing set, unseen during training, evaluates its performance on new instances. This separation guards against overfitting, where the model memorizes training data but fails on new data. Testing sets help detect such issues and ensure the model generalizes well. They act as a benchmark for model selection and provide confidence in real-world predictive accuracy, making the analytics process robust and reliable.

Eu costumo usar métodos de reamostragem, pois considero que o algoritmo n?o foi totalmente testado como uma amostragem única. Uso Random sampling, K-fold Cross-Validation (normalmente uso esse!) e Bootstrap.

Splitting of data into train, test and validation is a basic step. But it gets complicated when the dataset is small or needs time dependent validation, such as in forecasting models. N-fold cross validation could be helpful with smaller datasets. Out of time validation is usually helpful in most scenarios, specially in forecasting models. The split should be random avoiding any bias. Hence, data shuffling techniques and related parameter settings are important to understand and deploy when splitting the dataset.

Selecting an appropriate training and test set is improtant. Too small of a training set and you won't be able to effectively train the model, too small of a test set and you won't get enough data to reasonably conclude if the model is effective. A common method is the 80/20 rule, use 80% of the data to train the model and test on the other 20. However, this is not a hard rule, just a rough guideline. It's more important to look at your historic data and assess what the purpose of the model is. This can help determine a good break point based on where data relates to external notable events.

Additionally, data splitting strategies should account for concept drift - where relationships in the data change over time. Models should be reevaluated periodically on fresh test sets to check if accuracy drops due to evolving data patterns.

Off course! Cross-validation is one of the critical technique in machine learning which helps providing a robust means to assess model performances and enhance generalization capabilities. Its application is necessary for building models that perform well on unseen data, and can be considered for a regular evaluation in the development of reliable machine learning systems. However the only challenge it posseses is that it is recommended for a limited data set as may be expensive for larger data sets and modelling.

Cross-validation is a vital technique in predictive analytics for robustly validating the data analysis process. In K-Fold Cross-Validation, the dataset is divided into 'k' subsets, and the model is trained and validated 'k times, averaging performance metrics for reliability. Leave-One-Out Cross-Validation (LOOCV) trains on all but one data point, repeating for each, offering thorough evaluation but being computationally intensive. Stratified Cross-Validation maintains class proportions in folds, crucial for imbalanced datasets. Shuffle-Split Cross-Validation randomly shuffles and splits data for randomness. Cross-validation mitigates data partitioning impact, providing a reliable estimate of model performance and reducing overfitting risk

In my experience, using cross-validation techniques is a smart way to make sure predictive models work well. It's especially helpful with small or uneven datasets and when you're tweaking the model for better performance. It is better to ensures your model is strong and does a good job in different situations.

Se destaca la importancia de la validación cruzada en la reducción de la variabilidad y el sesgo durante la evaluación del modelo, lo cual es esencial para obtener estimaciones más confiables de la precisión del modelo. De esta manera en la etapa previa, podemos determinar si los datos tienen un alto grado de confiabilidad y por ende, tomar las acciones respectivas.

Dividing the dataset into multiple subsets, training the model on different subsets, and validating on the remaining parts to ensure robustness. Choose metrics based on the specific goals of your model and the characteristics of your data.

New data visualization tools with predictive AI are getting better and less costly, and they automatically give you some values that show you how accurate is the forecast as the p-value, and confidence interval. It is now not about how to calculate these values but how to interpret them to understand your data and forecast.

Consider the 'cost of misprediction'. Quantify the financial impact of false positives and negatives. For instance, in a customer churn model, assess the cost of wrongly predicting a loyal customer as a churn risk versus missing a real churn risk.

A quantile-quantile plot (QQ plot) is a quick and easy visual to see if your model is accurate. It opperates off the assumption that when you divide your data into a a train and test set, the predictions that your model returns likely won't be exact. In an accurate model, the errors between your observed result and your prediction should be normally distributed, since the only error will be random (not because you missed something in your model). The QQ plot is a method of plotting and visually determining if your model is accurate.

In classification models, understanding the difference in precision and recall and determining what is more important for the business problem is important. Simply, achieving a high accuracy score is not sufficient; it has to be seen in the context of the problem at hand. This analysed across training and validation datasets is key to understand and handle over-fitting / under-fitting scenarios. ROC curve or lift curve needs to be studied to determine whether the model is capturing more % of target audience in initial deciles or not. Also, it should capture more % of value and not just volume in top deciles when scoring the population.

In my experience always align your evaluation metric with your business goal and problem context. For Classification Models: ? Confusion Matrix: Understand errors by seeing true/false positives and negatives ? Accuracy: Simple but careful with imbalanced data! ? Precision & Recall: Great for when one type of error costs more than the other ?F1 Score: Balances precision and recall ?ROC & AUC: Measures ability to distinguish between classes For Regression Models: ?MAE & MSE: Measures average error in predictions ?RMSE: Similar to MSE, but more sensitive to large errors General Evaluation Techniques: ? Cross-Validation: Test model reliability with different data subsets ? Learning Curves: Check how well the model learns as data increases

Comparing baseline will be the FIRST step, which baseline equals to how was it done in the past without using technology to predict; get the team who were predicting/forecasting to criticise this framework/formula/and continue to work on them. Do not only count on the tech team, make them the dream team, encourage safe space for them to brain storm.

Understanding the operational value of the model output is important. For example the gains chart in a classification model tells us what % of the events have been captured in the first two-three deciles and what is the lift observed between the first and the last decile. Profiling the variables across the deciles of the probability scores and observing the trend can also indicate towards a robust model. For example if the net worth of a customer is positively impacting the model and we observe that the net worth of customers is consistently decreasing as we go from the top probability decile to the lowest decile, its an indication of a accurate model.

Importante comparar os modelos principalmente para verificar se n?o tem "data drift". Um exemplo disso foi na pandemia, em que os modelos ficaram desatualizados pela mudan?a no comportamento dos clientes durante esse período, deixando o modelo sem eficácia nas previs?o.

Benchmarking is a practice that I've found invaluable. It's essential to compare your predictive model's accuracy against a baseline or existing models. This helps you gauge whether your model provides a significant improvement. A well-chosen benchmark sets a standard for success.

As long as model, or ensemble of models are giving appropriate business outcome they are good. It doesn’t matter whether you got the best with Heuristic, Predictive, or any other mathematical / statistical model. It all depends upon the problem statement: objective. E.g: uniform deciles are important for credit lending, but not for marketing! Segmentation can be achieved with few business rules then why do we need to apply clustering etc.

I have often found it helpful to study whiter-papers or check Kaggle competitions to determine what "good" looks like in terms of model performance for a business problem. It also helps in tempering or setting expectations with stakeholders and prevents any arbitrary demands of very high levels of accuracy for the model. Handling expectations on the outcomes of a model is as important as building the model. There are enough examples where the data science project is successful but it never gets implemented. Hence, it is important that the project team sets and manages the expectations from the project with as much rigour as the project itself.

I have found that working on real world problems/datasets gives a more personalized approach and helps in understanding and relating the outcomes to the actual problem.

Real-world testing is the litmus test for your predictive model's accuracy. It's where the rubber meets the road. Apply your model to real-world data and scenarios to see how it performs in the field. It's a practice that reveals how well your model generalizes and adapts to changing conditions.

Consider Out of Time or Out of Sample testing. A true test of a model is when the model encounters data it has never seen. Real world problems often have some surprises in the data.

Beyond these steps, consider the importance of interpretability. Can you explain how your predictive model makes decisions? Interpretability is essential, especially in fields where regulatory compliance or ethical concerns come into play. Lastly, never stop iterating and fine-tuning your model. Predictive analytics is an ongoing journey of continuous improvement. Embrace feedback, adapt to changing data distributions, and refine your model to maintain accuracy over time.

Consider the space you are using, its amenities, lighting, furniture, and other stuff that can give a certain emotional direction to your focus group. You must give your attendees and incentive for the time they shared with you and make sure to thank them. Always remember that handling a focus group will always make you learn more stuff, and the more focus groups you make the better you get :D