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Answer: Apply representation transformation techniques such as normalization to standardize the data ranges, ensuring all features contribute equally to the learning process.
Normalization is a critical pre-processing step for Neural Networks, especially when dealing with datasets that have features with different ranges. It standardizes the data, facilitating faster convergence of the optimization algorithm and preventing it from being trapped in suboptimal local minima. Techniques like min-max normalization or z-score normalization can be applied to ensure uniformity across all features, thereby enhancing the model's ability to learn the relationships between features and the target variable effectively. This approach is both cost-effective and timely, aligning with the project's constraints.
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You are a Machine Learning Engineer working on a Neural Network project aimed at predicting housing prices. The dataset provided includes features such as square footage, number of bedrooms, and age of the property, each with varying ranges. During the model training phase, you observe that the gradient optimization process is inefficient, struggling to converge to an optimal solution due to the disparate feature scales. The project has tight deadlines and a limited budget, requiring a cost-effective and timely solution. What is the most effective action to take to address this issue? (Choose one correct option)
A
Enhance the data cleaning process by eliminating features that contain missing values, as this could simplify the model's learning process.
B
Apply representation transformation techniques such as normalization to standardize the data ranges, ensuring all features contribute equally to the learning process.
C
Modify the dataset partitioning to decrease the test set size, thereby increasing the training set size to provide more data for learning.
D
Employ feature construction to merge the most influential features together, reducing the dimensionality of the dataset.