Standardization Vs Normalization | Feature Scaling in Machine Learning | Intellipaat
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This video on Standardization vs Normalization covers concepts like Feature Scaling in Machine Learning in detail with examples. This part covers the basic differences between Standardization vs Normalisation and the use cases for each Standardization and Normalisation. In this part on Standardization vs Normalisation, we also deal with 2 datasets of different scenarios and learn how to Feature scale these datasets in Python using the popular Sci-Kit learn library.
Below are the concepts covered in this 'Tensors in PyTorch Tutorial':
00:00 - Why Feature Scaling is Important?
00:22 - Dataset with Outliers (Example 1)
01:25 - Dataset having varying values (Example 2)
02:20 - Normalisation
03:07 - Standardization
04:06 - Summary of Standardization vs Normalization
05:11 - Implementation of Standardization and Normalisation in Python
✅ What is the difference between standardization and normalization?
Standardization rescales your data so that it has a mean of 0 and a standard deviation of 1. On the other hand, Normalization is all about squeezing your data into a range between 0 and 1.
✅ Should I normalize or standardize my data?
There is a simple technique to figure out when to use Standardization vs Normalisation. Normalization is preferred in cases where the scale of your dataset varies significantly, whereas, Standardization is used when the dataset consists of outliers.
✅ Why Feature Scaling is important in machine learning?
Feature Scaling is essential to bring all your data to a standard scale, so that your entire dataset can contribute to the predictions equally, reducing any chance of bias. This in turn, increases the accuracy of the model. Hence Feature scaling plays an important role in Machine Learning.
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