6 Ways to Improve Your Predictive Models in Data Science

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In data science projects, building predictive models is a core task that requires not only technical savviness but also the ability to draft strategies to ensure success. From selecting the right predictor features to optimizing model performance, a well-structured approach is key. Whether you aim to create the perfect image classifier, sales predictor, or price estimator, the six practical tips listed in this article will guide you in building robust, accurate predictive models.

1. Select Relevant Features, Discard Irrelevant Ones

 
Select the most influential data variables for your predictive model, removing irrelevant or redundant ones. From correlation analysis to domain expert knowledge, there are multiple approaches to select the relevant predictor features that will act as your predictive model inputs to be “translated” into predicted outcomes. For instance, in a sales prediction model, factors like seasonality or marketing campaign characteristics might be more relevant than buyers’ age or ethnicity.

2. Clean, Prepare, and Improve your Relevant Data

 
Once your relevant data have been identified, make sure they are free from errors, inconsistencies, or atypical values, and ensure they have sufficient quality. On top of that, apply normalization or standardization on some numerical features if necessary: many predictive models are more accurate when data fed to them are normalized.
In the previous sales prediction example, you may want to fix incorrect sales data and unify multiple currencies across regions before building the model.

3. Explore Multiple Models and Approaches

 
Do not limit yourself to building or training one single type of predictive model to address your data science problem. Most predictive models today rely on machine learning (ML) techniques but do not forget there are traditional predictive modeling approaches from statistics that might sometimes be sufficient. If sticking to training an ML model, like a classifier, a regressor, or a time series forecasting model, be aware of the variety of model types and techniques available for addressing each of these predictive tasks. For instance, a regression model to predict house prices could be based on linear regression, decision trees, or random forest ensembles. Compare the preliminary results and efficiency of each model type to filter the most promising one(s).

4. Cross-validation

 
Cross-validation is an effective evaluation approach for ML-based predictive models, to ensure not only they learn well from the data they have been exposed to, but also they can generalize well to future data and make accurate predictions. The approach consists of dividing the data into different train-test combinations, evaluating each combination separately, and averaging results.

5. Fine-Tune Promising Models and Approaches

 
After identifying the most promising model types and applying cross-validation on ML ones to ensure they are generalizable, why not seek an even better performance by applying extra adjustments on their internal gears? That’s the purpose of techniques like hyperparameter tuning, based on search algorithms that seek the most promising combinations of manually set model parameters: just like finding the best combination of enabled and disabled switches in a huge control panel.

6. Implement Continuous Feedback and Re-Training Mechanisms

 
Once deployed, continuously monitor your predictive model and retrain it regularly on new data to reflect changes in the real-world data it consumes to make predictions. For example, a product demand forecasting model needs continuous adjustments to adapt to constantly changing market trends. Look out for data drifts, or deviations in the statistical properties of the consumed data that may seriously deteriorate model performance.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.