5. How Do Feature Scales and Distributions Affect Supervised Learning?
Feature scales and distributions can greatly affect how well supervised learning models perform.
Confusing Importance: When different features are on different scales, it can create confusion. This means some features may seem more important than they really are. For example, if one feature ranges from 0 to 1 and another ranges from 0 to 10, the second one might unfairly take over the learning process.
Struggling to Learn: Some algorithms, like Gradient Descent, might learn slowly or get stuck in certain patterns if the scales are not consistent. This happens especially when features have different distributions.
Overreacting to Outliers: When some features have uneven distributions, the models can become too sensitive to unusual data points (called outliers). This can lead to wrong predictions.
To fix these problems, we can use feature scaling methods, like Standardization or Min-Max Scaling. These techniques help make distributions more consistent, which can lead to better learning results.
5. How Do Feature Scales and Distributions Affect Supervised Learning?
Feature scales and distributions can greatly affect how well supervised learning models perform.
Confusing Importance: When different features are on different scales, it can create confusion. This means some features may seem more important than they really are. For example, if one feature ranges from 0 to 1 and another ranges from 0 to 10, the second one might unfairly take over the learning process.
Struggling to Learn: Some algorithms, like Gradient Descent, might learn slowly or get stuck in certain patterns if the scales are not consistent. This happens especially when features have different distributions.
Overreacting to Outliers: When some features have uneven distributions, the models can become too sensitive to unusual data points (called outliers). This can lead to wrong predictions.
To fix these problems, we can use feature scaling methods, like Standardization or Min-Max Scaling. These techniques help make distributions more consistent, which can lead to better learning results.