Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors

Normalizing Data Sets in Machine Learning

Photo of author

By Team EasyExamNotes

Normalizing data is a common preprocessing step in machine learning. It involves transforming the data so that it has a zero mean and a unit variance.

This can be helpful for several reasons, including:

1. Improved performance of algorithms: Many machine learning algorithms, such as linear regression and logistic regression, assume that the data is normally distributed. Normalizing the data can help to improve the performance of these algorithms.

2. Reduced sensitivity to outliers: Normalizing the data can help to reduce the sensitivity of machine learning algorithms to outliers. Outliers are data points that are far away from the rest of the data. They can have a large impact on the training of a model, and normalizing the data can help to mitigate this effect.

3. Prevents numerical instability: Normalizing the data can help to prevent numerical instability in machine learning algorithms. Numerical instability can occur when the data has a large range of values, and it can lead to inaccurate results.

Some common methods for Normalizing Data

1. Mean normalization: This method subtracts the mean of the data from each data point.

2. Min-max normalization: This method scales the data to a fixed range, such as [0, 1] or [-1, 1].

3. Z-score normalization: This method subtracts the mean of the data from each data point and then divides by the standard deviation.

References:

  • Preprocessing Data in Machine Learning by Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, 2011.
  • Data Preprocessing for Machine Learning by D.J. Hand, C.C. Taylor, C.A. Nimblett, G.M. Currie, P.J. Hall, D.J. McClean, Data Mining in Practice, Elsevier, 2000.
Download as PDF

Related posts:

  1. Explain computer vision with an appropriate example
  2. Explain Reinformcement learning with an appropriate exaple
  3. Reinforcement Learning Framework
  4. Data augmentation
  5. Machine learning models
  6. Unsupervised machine learning
  7. Neural Network in Machine Learning
  8. Recurrent neural network
  9. Support Vector Machines
  10. Long short-term memory (LSTM) networks
  11. Convolutional neural network
  12. Define machine learning and explain its importance in real-world applications.
  13. Differences Between Machine Learning and Artificial Intelligence
  14. Machine Learning works on which type of data ?
  15. What is Regression in Machine learning
  16. Finding Machine Learning Datasets
  17. What is hypothesis function and testing
  18. How to implement Convolutional neural network in Python
  19. What does it mean to train a model on a dataset ?
  20. Can a textual dataset be used with an openCV?
  21. Name some popular machine learning libraries.
  22. Introduction to Machine Learning
  23. Explain the machine learning concept by taking an example. Describe the perspective and issues in machine learning.
  24. What is the role of preprocessing of data in machine learning? Why it is needed?
  25. Explain the unsupervised model of machine learning in detail with an example.
  26. What is Machine learning ?
  27. What is Machine Learning ?
  28. Types of Machine Learning ?
  29. Applications of Machine Learning
  30. Data Preprocessing
  31. Data Cleaning
  32. Handling Missing Data
  33. Feature Scaling
  34. Labeled data in Machine learning
  35. Difference between Supervised vs Unsupervised vs Reinforcement learning
  36. Machine learning algorithms for Big data
  37. Difference between Supervised vs Unsupervised vs Reinforcement learning
  38. What is training data in Machine learning
  39. What is Ordinary Least Squares (OLS) estimation
  40. Scalar in Machine Learning
  41. Scalars in Loss Functions | Machine Learning
  42. Linear Algebra for Machine Learning Practitioners
  43. Supervised Learning
  44. Top Interview Questions and Answers for Supervised Learning
  45. What are the different types of machine learning?
  46. What is a hyperparameter in machine learning ?
  47. Unsupervised Learning Interview Q&A
  48. TOP INTERVIEW QUESTIONS AND ANSWERS FOR Artificial Intelligence
  49. Deep Learning Top Interview Questions and Answers
  50. What is target variable and independent variable in machine learning
  51. Machine Learning Scope and Limitations
  52. Statistics and linear algebra for machine learning
  53. What is MNIST ?
  54. Some real time examples of machine learning