Limitations of Predictive Analytics(2)

 Bilal Hussain Malik

    

                                                Limitations of Predictive Analytics

                                                      Overfitting or Underfitting

In predictive analytics, proper model complexity is the secret to acquiring accurate and trustworthy results. Two of the most common problems in building models are overfitting and underfitting, both of which compromise a model's ability to perform well on new data.

Overfitting occurs when a model is too intricate and it not only learns the actual patterns of the training set but also the noise and irregularities. The result is that the model performs highly on the training set but fails to make accurate predictions on new data. This is because the model has gotten too customized towards the training set, becoming sensitive to slight changes and losing its ability to generalize.

On the other hand, underfitting takes place when a model is oversimplified to recognize patterns and relationships within the data. It fails to learn enough from the training set and will generally do poorly on both training and test sets. Underfitted models usually ignore important features or make too simplifying assumptions that lack real-world complexity.

Both overfitting and underfitting reduce the effectiveness of predictive analytics in real-world applications by producing misleading or unreliable predictions. Data scientists avoid these issues arising by utilizing a variety of techniques such as cross-validation, regularization techniques, and model parameter tuning. Selecting a suitable algorithm and model parameter fine-tuning using testing cycles accomplishes a balanced trade between accuracy and generalization.

Lastly, model design and evaluation with caution are important to make predictive models robust, adaptable, and efficient when applied to real-world scenarios