IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

Question: 1 / 400

What is the main goal of feature selection in machine learning?

To increase the number of variables in the model

To reduce computational load without impacting model performance

The primary goal of feature selection in machine learning is to reduce the number of input variables for a model while maintaining or improving its performance. By selecting the most relevant features, you can decrease the computational load significantly. This reduction in dimensionality not only speeds up the model training process but also minimizes the risk of overfitting, where the model learns noise from the training data rather than generalizable patterns.

Effective feature selection can enhance the performance of various models by eliminating irrelevant or redundant variables that do not contribute meaningful information, thus optimizing the model's efficiency and robustness. By focusing on the most impactful features, practitioners can achieve better interpretability and potentially higher accuracy in predictions.

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To improve data visualization techniques

To ensure all variables contribute equally to the model

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