What are features of Confidence Interval ?
- Range of Values: A confidence interval gives us a range of values, not just a single number, to estimate where the true population parameter might lie.
- Level of Confidence: It tells us how confident we are that the true parameter falls within that range. For example, a 95% confidence interval means we are 95% confident the true parameter is in the interval.
- Based on Sample Data: Confidence intervals are calculated based on a sample from the population, not the entire population itself.
- Quantifies Uncertainty: They help quantify the uncertainty or variability in our estimates, considering that samples can vary from one another.
- Statistical Tool: Confidence intervals are a statistical tool used to make inferences about population parameters from sample data.
- Interpretation: It’s important to interpret confidence intervals correctly. They do not give the probability that the true parameter is within the interval; instead, they reflect the long-term success rate of the estimation procedure.
Confidence Intervals for Machine Learning
In machine learning, confidence intervals play a crucial role in quantifying the uncertainty associated with model predictions and parameter estimates. They provide a range of values within which we can be confident that the true value lies, given a certain level of probability. In this article, we will see confidence intervals relevance in Machine Learning.