How can feature scaling affect the performance of certain Machine Learning algorithms?
- It changes the distribution of the data
- It helps algorithms converge faster and perform better
- It increases the computational complexity of the model
- It increases the number of features
Feature scaling normalizes or standardizes the features, making them all on a similar scale. This can help gradient descent-based algorithms converge faster and may lead to better performance for distance-based algorithms like KNN.
Loading...
Related Quiz
- Unlike PCA, which assumes that the data components are orthogonally distributed, ICA assumes that the components are ________.
- While supervised learning requires explicit labels, ________ learning operates on data without explicit instructions.
- Can classification be used to predict continuous values?
- When using Bootstrapping for estimating the standard error of a statistic, the process involves repeatedly resampling the data ________ times.
- A bank uses a machine learning model for loan approvals. However, it's observed that individuals from certain ethnic backgrounds are consistently getting rejected more than others, despite having similar financial profiles. This raises concerns related to which aspect of machine learning?