IBM Data Science Practice Test

In which scenario is data normalization particularly beneficial?

1. Storing multiple variables in one column

2. Updates occurring too frequently

3. Model training efficiency

4. Connecting datasets from different sources

The correct answer is: Model training efficiency

Data normalization is a crucial step in preparing data for modeling, particularly when it comes to improving model training efficiency. This process involves scaling the features of the dataset to ensure they contribute equally to distance calculations in algorithms used during training, such as k-nearest neighbors or gradient descent-based methods. When datasets contain features with varying ranges or units, some features can dominate the distance computations, leading to biased models. Normalization addresses this issue by transforming features to a common scale, which helps in speeding up convergence during training and can lead to better performance overall. By ensuring all input features are on the same scale, normalization can reduce the impact of outliers and enhance the capability of models to learn meaningful patterns from the data. While connecting datasets from different sources may suggest a need for normalization to ensure consistency, the primary focus in this context is the direct impact of feature scaling on the efficiency and effectiveness of model training. Each of the other scenarios mentioned could require different data handling strategies but does not specifically highlight the importance of normalization in training models.