What are some best practices for scaling and normalizing numerical features in pandas?

1 Why scale and normalize?

Numerical features can have different ranges, units, and magnitudes, which can affect how machine learning algorithms learn from them. For example, if one feature has values between 0 and 1, and another has values between 0 and 1000, the latter will dominate the former in terms of influence and variance. Scaling and normalizing can help standardize the features and make them comparable and consistent.

2 How to scale and normalize?

There are various methods and techniques that can be used to scale and normalize numerical features, depending on the goal and data characteristics. Min-max scaling transforms values to a fixed range, usually between 0 and 1, while standardization transforms them to have a mean of zero and a standard deviation of one. Normalization ensures that features have equal importance and magnitude by transforming the values to have a unit norm. In pandas, these methods can be applied using the

apply        

or

transform        

methods on a DataFrame or Series, with custom or predefined functions from the

sklearn.preprocessing        

module such as

MinMaxScaler        

,

StandardScaler        

, or

Normalizer        

.

3 What to consider before scaling and normalizing?

Before scaling and normalizing your numerical features, it is important to consider some factors that may affect your choice of method and outcome. These include the type and distribution of your data, the goal and assumptions of your machine learning model, as well as the interpretability and explainability of your features. For instance, standardization may be more suitable for data that follows a normal distribution, while min-max scaling may be more suitable for data that has a fixed range. Moreover, models that use distance-based metrics may perform better with scaled and normalized features, while models that use tree-based algorithms may not be affected. Additionally, scaling and normalizing may change the meaning and significance of your features, making them less intuitive or altering their relative importance and correlation.

4 How to evaluate and compare scaling and normalizing methods?

After scaling and normalizing your numerical features, you should evaluate and compare the results of different methods to determine which is most suitable for your data and model. Visualizing the distributions and relationships of your features with plots, such as histograms, boxplots, scatterplots, or heatmaps can help you observe how scaling and normalizing affect the shape, spread, and outliers of your features, as well as the correlation and covariance among them. Additionally, computing summary statistics and metrics such as mean, median, standard deviation, range, skewness, kurtosis, or variance inflation factor can assist in quantifying how scaling and normalizing affect the central tendency, variability, symmetry, and multicollinearity of your features. Lastly, training and testing your machine learning model using different methods while measuring performance such as accuracy, precision, recall or F1-score can help you evaluate how scaling and normalizing impact the predictive power and generalization of your model.

5 How to avoid common pitfalls and mistakes when scaling and normalizing?

Scaling and normalizing numerical features can be a powerful and useful technique, but it can also introduce some pitfalls and mistakes if not done properly or carefully. To avoid these common issues, it is important to split your data into train and test sets before scaling and normalizing. Additionally, you should only scale and normalize numerical features that are continuous or interval, as doing so to categorical or ordinal features can distort their meaning and order. Furthermore, it is essential to keep a copy of your original values or store the parameters used for scaling and normalizing, so that you can reverse or compare your features if necessary.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?