Test Your Knowledge
Question 1 of 4
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 4
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 4
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 4
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