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Question 1 of 8
What is the primary purpose of normalization in machine learning?
To increase the range of values in a dataset
To reduce the number of features in a dataset
To scale features to a common range
To eliminate outliers from a dataset
Question 2 of 8
Which of the following is NOT a benefit of using normalization?
Improved algorithm convergence
Better comparability between features
Preservation of original value interpretation
Reduced impact of different units of measurement
Question 3 of 8
What is the formula used by Min-Max Scaler for normalization?
(x - mean) / standard deviation
(x - min) / (max - min)
(x - median) / (max - median)
x / max
Question 4 of 8
In which scenario might normalization be less beneficial or even detrimental?
When working with neural networks
When using distance-based algorithms like K-Nearest Neighbors
When the dataset contains significant outliers
When all features are already on the same scale
Question 5 of 8
What is the primary goal of standardization in machine learning?
To remove outliers from the dataset
To transform features to have a mean of 0 and standard deviation of 1
To scale features between 0 and 1
To reduce the number of features in the dataset
Question 6 of 8
Which of the following is NOT a benefit of using standardization?
It helps meet the normal distribution assumption
It's more robust to outliers than normalization
It preserves the original unit interpretation
It improves the performance of many machine learning algorithms
Question 7 of 8
In which scenario might standardization be particularly beneficial?
When working with categorical data
When using algorithms that assume normal distribution of data
When the dataset has no outliers
Question 8 of 8
What is a potential drawback of applying standardization to a dataset?
It always removes important information from the data
It makes the algorithm more sensitive to outliers
It can make it difficult to interpret the data in its original context
It always decreases the performance of machine learning models