Why is Bootstrapping an essential technique in statistical analysis?
- It allows training deep learning models
- It enables the estimation of the distribution of a statistic
- It provides a method for feature selection
- It speeds up computation
Bootstrapping is essential in statistical analysis because it allows estimating the distribution of a statistic, even with a small sample. By repeatedly resampling with replacement, it creates numerous "bootstrap samples," enabling the calculation of standard errors, confidence intervals, and other statistical properties.
Loading...
Related Quiz
- What is Gradient Boosting, and how does it work?
- If you're working with high-dimensional data and you want to reduce its dimensionality for visualization without necessarily preserving the global structure, which method would be apt?
- What is the bias-variance tradeoff in Machine Learning?
- In a situation where the training accuracy is high but the testing accuracy is low, what could be the issue, and how might you solve it?
- Explain the concept of regularization in Machine Learning. What are some common techniques?