Exam Professional Machine Learning Engineer topic 1 question 58 discussion

Vote for B. We could impute instead of remove the column to avoid loss of information

I also think it is B: "The presence of feature value X in the formula will affect the step size of the gradient descent. The difference in ranges of features will cause different step sizes for each feature. To ensure that the gradient descent moves smoothly towards the minima and that the steps for gradient descent are updated at the same rate for all the features, we scale the data before feeding it to the model."

clearly B

B) Option B (Use the representation transformation technique) is the most relevant choice. Normalizing the features will help gradient descent converge efficiently, leading to better weight updates and improved model performance. Remember that feature scaling is crucial for gradient optimization, especially when dealing with features that have different ranges. By ensuring consistent scales, you’ll enhance the effectiveness of your Neural Network training process.

Because the range needs to normalize

B - The key phrase is "different ranges", therefore we need to normalize the values.

Went with B

Normalization

Normalization is the word.

Normalization is the word.

B "Normalization" is the keyword

normalization https://developers.google.com/machine-learning/data-prep/transform/transform-numeric

B. The problem does not mention anything about missing values. It needs to normalize the features with different ranges.

Looking at explanation I would choose C as well

(B) - NN models needs features with close ranges - SGD converges well using features in [0, 1] scale - The question specifically mention "different ranges" Documentation - https://developers.google.com/machine-learning/data-prep/transform/transform-numeric

When gradient descent fails, it's out of the lacking of a powerful feature. Using normalization would make it worse. Instead, using either A or C would increase the strength of certain feature. But, C should come first since A is only feasible after at least 1 meaningful training. So C.

B - remove the outliers?