Normalization vs Standardization: When to use which
Feature engineering plays a vital role in creating precise and efficient machine learning models. A crucial component of this process involves scaling, normalization, and standardization, wherein data is transformed to enhance its modeling compatibility. These methods can enhance model performance, mitigate the influence of outliers, and guarantee data consistency. This article delves into the notions of scaling, normalization, and standardization, outlining their significance and practical implementation across various data types.
What is Feature Scaling?
Feature scaling is a crucial preprocessing technique that transforms feature values in a dataset to a uniform scale, preventing the dominance of features with larger values and ensuring equitable contribution to the model. It becomes vital when dealing with disparate feature ranges, units, or magnitudes, which could otherwise bias model performance or hinder learning. Standardization, normalization, and min-max scaling are common scaling techniques that maintain feature relationships and distributions. Scaling harmonizes feature scales, streamlining the construction of precise and efficient machine learning models by allowing meaningful feature comparisons, enhancing model interpretability, and reducing the impact of outliers.
When should we use feature scaling?
1- Gradient based approaches: Having features on a similar scale can help the gradient descent converge more quickly towards the minima. (e.g. SGD)
2- Distance-based algorithm: scale the data before employing a distance based algorithm so that all the features contribute equally to the result. (e.g. KNN)
3_ Not in tree-based algorithm: The decision tree splits a node on a feature that increases the homogeneity of the node. Other features do not influence this split on a feature
– Normalization
Normalization serves as a data preprocessing method, aligning feature values within a dataset onto a shared scale. Its purpose is to simplify data analysis and modeling while minimizing the influence of varying scales on machine learning model accuracy. This technique, also termed Min-Max scaling, involves adjusting values to span a range from 0 to 1.
– Standardization
Standardization often referred to as z-score Normalization, is another scaling method where the values are centered around the mean with a unit standard deviation. This means that the mean of the attribute becomes zero, and the resultant distribution has a unit standard deviation.
Comparison
| Normalization | Standardization |
| Rescales values to a range between 0 and 1 | Centers data around the mean and scales to a standard deviation of 1 |
| Useful when the distribution of the data is unknown or not Gaussian | Useful when the distribution of the data is Gaussian or unknown |
| Sensitive to outliers | Less sensitive to outliers |
| Retains the shape of the original distribution | Changes the shape of the original distribution |
| May not preserve the relationships between the data points | Preserves the relationships between the data points |
| Equation: (x – min)/(max – min) | Equation: (x – mean)/standard deviation |
When to use which
Normalization is an effective option for data with non-Gaussian distributions, aiding in improved model performance and accuracy. It is particularly beneficial when uncertainties surround feature distributions, signifying non-Gaussian patterns. Notably, normalization accommodates outliers that demand a broader functional range. In contrast, standardization proves useful when your data exhibits a Gaussian distribution and is well understood. Unlike normalization, standardization isn’t bound by specific ranges and doesn’t affect outliers. Normalization operates within [0,1] or [-1,1], while standardization lacks range limitations. The choice between them hinges on algorithm assumptions: normalization suits when distributions are unknown, and standardization when predictions about data distributions are viable.
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