In which scenario would Min-Max normalization be a less ideal choice for data scaling?
- When outliers are present
- When the data has a normal distribution
- When the data will be used for regression analysis
- When interpretability of features is crucial
Min-Max normalization can be sensitive to outliers. If outliers are present in the data, this scaling method can compress the majority of data points into a narrow range, making it less suitable for preserving the information in the presence of outliers. In scenarios where outliers are a concern, alternative scaling methods like Robust Scaling may be preferred.
Loading...
Related Quiz
- In time-series data, creating lag features involves using previous time steps as new _______.
- Which activation function is commonly used in the output layer of a binary classification neural network?
- In the Data Science Life Cycle, which step involves defining the objectives and understanding the problem statement?
- For real-time object detection in images or videos, the _______ algorithm is widely adopted.
- An AI startup with limited computational resources is building an image classifier. They don't have the capability to train a deep neural network from scratch. What approach can they use to leverage the capabilities of deep learning without the extensive training time?