What is the difference between standardized and normalized

Get FREE domain for 1st year and build your brand new site. In Machine Learning Data Preprocessing is one of the most important steps to gain key insights from Data. Some of the buzz words mentioned above are statistical terms used for cleaning Numerical Data so that different ML models can predict efficiently.

After cleaning come the part where we have to optimize the model to get accurate results this and avoid overfitting which is another topic all-together! You may have heard of Data Cleaning for categorical data to easily interpret the results, but what about Numerical Data. Aren't we just supposed to feed Numerical data and get results from the model. This is not the case always.

A simple reason is this : A given Dataset contains data from various distributions and ranges, which which may or may not be equal. While applying some ML Algorithms,we have certain assumptions about the distributions which when not met can give inaccurate results. Coming to the Range, huge differences in data values of different variable can falsely influence a variable due to its large values even leading to not getting a feasible solution, therefore we clean the numerical data through Standardization, Regularization and Normalization.

Let's discuss them in detail! In ML we have preprocess the data, and we train the model. We have a training data and a testing data.

In cases when we don't have the testing data, we split the training data for the test data. The more we train the better results we get, right? Machine Learning isn't that simple. Here comes the concept of Overfitting and Underfitting. Attention reader! Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready. There are some feature scaling techniques such as Normalization and Standardization that are the most popular and at the same time, the most confusing ones.

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Normalisation Standardisation 1. Minimum and maximum value of features are used for scaling Mean and standard deviation is used for scaling. It is used when features are of different scales. Scaling being one of them. You can also log the data, or do anything else you want. The type of normalisation you use would depend on the outcome you want, since all normalisations transform the data into something else.

Here some of what I consider normalization examples. Scaling normalisations Quantile normalisation. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. What's the difference between Normalization and Standardization? Ask Question. Asked 10 years, 6 months ago. Active 1 month ago. Viewed k times. Any article or chapters of books for reference would be much appreciated.

Also here's another example of what I'm trying to do. Would that be normalization? Improve this question. Chris Chris 1, 3 3 gold badges 11 11 silver badges 3 3 bronze badges. For more on that see the question and replies at stats. In particular, note that neither normalization nor standardization have any direct relevance to the Dean's problem.

Add a comment. Active Oldest Votes. Improve this answer. Vivek Kumar Vivek Kumar 1, 1 1 gold badge 7 7 silver badges 2 2 bronze badges. For instance, if you divided every number in your dataset by the max value e.

Outliers typically implies samples beyond X percentile of the distribution. All you do with min max normalization is to scale the values. The relative distance between samples would stay the same.