Why standardize variables

As seen above, for distance based models, standardization is performed to prevent features with wider ranges from dominating the distance metric. But the reason we standardize data is not the same for all machine learning models, and differs from one model to another. Clustering models are distance based algorithms, in order to measure similarities between observations and form clusters they use a distance metric. So, features with high ranges will have a bigger influence on the clustering.

Therefore, standardization is required before building a clustering model. Standardization makes all variables to contribute equally to the similarity measures. Support Vector Machine tries to maximize the distance between the separating plane and the support vectors. If one feature has very large values, it will dominate over other features when calculating the distance. So Standardization gives all features the same influence on the distance metric.

You can measure variable importance in regression analysis, by fitting a regression model using the standardized independent variables and comparing the absolute value of their standardized coefficients.

But, if the independent variables are not standardized, comparing their coefficients becomes meaningless. LASSO and Ridge regressions place a penalty on the magnitude of the coefficients associated to each variable. And the scale of variables will affect how much penalty will be applied on their coefficients. The table of summary statistics shown below demonstrates that both variables are indeed standardized.

Standardizing variables is not difficult, but to make this process easier, and less error prone, you can use the egen command to make standardized variables. The commands below standardize the values of math , science , and socst , creating three new variables, z2math , z2science , and z2socst. Again we can look at a table of summary statistics to confirm that these variables are standardized. I was more than happy to find this web site.

I need to thank you for your Best custom essay writing moment due to this unbelievable read!! I definitely savored every bit of it and I have you book-marked to see new things in your web site. I am novice in Data Science, Could you please also mention packages which needs to import for doing these calculations. What is the need of performing a standardization. If is not a implementing our model performance.

Congratulations on your comprehensive and easy-readable post on something so important for any modern data engineer. I would like to add from personal experience that certain monotonic functions e. See below example of the metrics from the same model with the same observed and predicted values, but with results in dollars left and pesos right. This is the scalability problem of the metrics. Does it matter if the variables that you are scaling are normally distributed or not?

What if the range is zero for some observations? In the case of the range method for example, the divisor would be zero for these observations. Your regression model almost certainly has an excessive amount of multicollinearity if it contains polynomial or interaction terms. Fortunately, standardizing the predictors is an easy way to reduce multicollinearity and the associated problems that are caused by these higher-order terms. All you need to do is click the Coding button in the main dialog and choose an option from Standardize continuous predictors.

These two methods reduce the amount of multicollinearity. In my experience, both methods produce equivalent results. Subtracting the mean is also known as centering the variables. Conveniently, you can usually interpret the regression coefficients in the normal manner even though you have standardized the variables. Minitab uses the coded values to fit the model, but it converts the coded coefficient back into the uncoded or natural values —as long as you fit a hierarchical model.

Consequently, this feature is easy to use and the results are easy to interpret. This example comes from a previous post where I show how to compare regression slopes.