![]() It can also be a good idea to scale the target variable for regression predictive modeling problems to make the problem easier to learn, most notably in the case of neural network models. , Data Mining: Practical Machine Learning Tools and Techniques, 2016. Consequently, it is usual to normalize all attribute values … There are also algorithms that are unaffected by the scale of numerical input variables, most notably decision trees and ensembles of trees, like random forest.ĭifferent attributes are measured on different scales, so if the Euclidean distance formula were used directly, the effect of some attributes might be completely dwarfed by others that had larger scales of measurement. , Feature Engineering and Selection, 2019.Īlso, algorithms that use distance measures between examples or exemplars are affected, such as k-nearest neighbors and support vector machines. This difference in scale for input variables does not affect all machine learning algorithms.įor example, algorithms that fit a model that use a weighted sum of input variables are affected, such as linear regression, logistic regression, and artificial neural networks (deep learning).įor example, when the distance or dot products between predictors are used (such as K-nearest neighbors or support vector machines) or when the variables are required to be a common scale in order to apply a penalty, a standardization procedure is essential. , Neural Networks for Pattern Recognition, 1995. One of the most common forms of pre-processing consists of a simple linear rescaling of the input variables. A model with large weight values is often unstable, meaning that it may suffer from poor performance during learning and sensitivity to input values resulting in higher generalization error. a spread of hundreds or thousands of units) can result in a model that learns large weight values. ![]() An example of this is that large input values (e.g. feet, kilometers, and hours) that, in turn, may mean the variables have different scales.ĭifferences in the scales across input variables may increase the difficulty of the problem being modeled. ![]() Input variables may have different units (e.g. Machine learning models learn a mapping from input variables to an output variable.Īs such, the scale and distribution of the data drawn from the domain may be different for each variable. This tutorial is divided into six parts they are: Photo by Marco Verch, some rights reserved. How to Use StandardScaler and MinMaxScaler Transforms Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.
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