Which factor is essential for the effectiveness of predictors in a model?

The relevance to the target variable is crucial for the effectiveness of predictors in a model because it ensures that the features used for prediction have a direct and meaningful relationship with the outcome being forecasted. If the predictors do not correlate with or influence the target variable, the model may produce inaccurate or nonsensical results. To build a robust model, it is essential to select features that can provide valuable information about the patterns or trends in the target variable, thereby enhancing the model's ability to make accurate predictions.

In contrast, while the number of data points available can improve the model's performance by providing a more comprehensive view of the underlying patterns, it is the relevance of the predictors that plays a central role in determining how well the model can generalize from the training data to unseen data. Computational power can facilitate the training of more complex models but does not inherently improve the model's predictive capabilities if the chosen features are not relevant. Similarly, the complexity of the model itself does not guarantee that it will perform well; a simpler model with relevant predictors can often outperform a more complex one with irrelevant predictors.