Scalers: Standard vs MinMax

Scaling is an essential step in data preprocessing, as it helps ensure that machine learning models treat all features equally and make more accurate predictions. When it comes to scaling the two most common techniques are

• Standardization (also known as Z-scoring or Z-score Normalization)

• MinMax Scaling (also known as Normalization or Rescaling)

The choice between the two depends on the nature of your data and the specific requirements of your machine learning algorithm.

Standardization (Z-score Normalization )

• Subtracts the mean and divides by the standard deviation for each feature

• Resulting distribution has a mean of 0 and a standard deviation of 1

• Useful when features have different units or scales

• Preserves the shape of the original distribution

• Related scikit-learn module for standardization is StandardScaler.

RobustScaler, another module for scaling the data, operates similarly to StandardScaler, and ensures that the features are on the same scale. The difference between the two is that RobustScaler uses the median and the quartiles (i.e. percentiles) instead; and therefore not influenced by a few very large values, i.e. outliers, in the dataset!

Min-Max Scaling (Normalization)

• Subtracts the Min and divides by the range (Max - Min) for each feature

• Rescales each feature to a common range (usually between 0 and 1)

• Useful when features have different ranges or units

• Can help reduce the effect of outliers

• Can change the shape of the original distribution

• Related scikit-learn module for normalization is MinMaxScaler.

Another module that is similar to MinMaxScaler is called, MaxAbsScaler which maps the original values different ranges depending on whether the dataset has negative OR positive values.

• If only positive values are present, the range is [0, 1] (same as MinMaxScaler).

• If only negative values are present, the range is [-1, 0].

• If both negative and positive values are present, the range is [-1, 1].

Key differences and nuances

• Standardization is more sensitive to outliers, as it uses the mean and standard deviation, which can be influenced by extreme values.

• Min-Max Scaling is more robust to outliers, as the normalized values are bounded between 0 and 1, which can reduce the impact of outliers.

• Standardization is more suitable for algorithms that assume normality or equal variances, such as Linear Discriminant Analysis (LDA) or Gaussian Naive Bayes.

• Min-Max Scaling is more suitable for algorithms that don't make assumptions about the distribution, such as Decision Trees or Support Vector Machines (SVMs).

• If your data has negative values or a large range, Min-Max Scaling might be more appropriate. If your data is already somewhat normalized or has a small range, Standardization might be sufficient.

Conclusion

In addition to above mentioned techniques, there are scaling methods, including but not limited to Normalizer (rescaling the vector for each sample to have unit norm), Log scaling (useful for skewed distributions), feature clipping (caps all feature values above (or below) a certain value to a fixed value), and custom scaling to a specific range. It is a general data preprocessing technique used in various supervised and unsupervised learning context, and is a versatile technique that benefits various applications, and help

• Reduce feature dominance

• Improve model performance

• Enhance generalization

• Identify patterns and relationships

• Prepare data for complex models

• Create informative visualizations

• Create new features

In conclusion, scaling is a crucial preprocessing step that ensures machine learning models treat all features equally, leading to more accurate predictions.

Further Reading

This article from scikit-learn is a good source that compares the effect of different scaling methods on a dataset with outliers.