Why Dimensionality Reduction Is Crucial in Machine Learning Models?

  • What is high dimensionality?
  • What are the difficulties caused by high dimensionality?
  • What is curse of dimensionality?
  • What are the benefits of dimensionality reduction?

What is high dimensionality?

  • To reduce complexity of model
  • To prevent overfitting
  • To achieve computational efficiency

What are the difficulties caused by high dimensionality?

  • It may lead to high computational cost.
  • It may cause overfitting during training of the model that means the model performs well during training, but performance accuracy degrades during testing on new data samples.
  • The higher the number of features variables throws more difficulties to visualize the training set.
  • High dimensionality may also have more chances to high correlation in data.

What is curse of dimensionality?

What are the benefits of dimensionality reduction?

  • It eliminates insignificant features from the dataset which improves the performance of the model because irrelevant features or noise leaves an adverse impact on the performance accuracy of machine leaning model.
  • It is useful to lower model training time and lower the data storage requirement.
  • It prevents from curse of dimensionality
  • It eliminates multi collinearity which results in better performance of the model.

Conclusion

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Dr. Virendra Kumar Shrivastava

Dr. Virendra Kumar Shrivastava

Professor || Alliance College of Engineering and Design || Alliance University || Writer || Big Data Analytics