ML Interview Q Series: Can you use different Normalization methods on different features?

📚 Browse the full ML Interview series here.

Comprehensive Explanation

It is absolutely possible and sometimes highly beneficial to use different normalization methods on different features. Each feature in a dataset can have unique properties such as range, distribution shape, presence of outliers, or domain-specific constraints. By choosing the most suitable normalization technique for each feature, you can often achieve better model performance, improved convergence during training, and more robust evaluation.

Reasons for Using Different Methods

Features with Different Scales Some features may vary from 0 to 1, while others might range from hundreds to thousands. A single normalization scheme might not be ideal for all these scales.

Varying Distributions Certain features might be normally distributed, while others might have heavy tails or extreme skew. A transformation that works well for a normal distribution (for instance z-score standardization) may be less effective for a feature with many outliers or a skewed distribution.

Outliers in Specific Features Some features might be very sensitive to outliers, so using a robust scaling approach (e.g. based on median and interquartile range) may be more suitable for those. In contrast, more normally distributed features might work well with standard z-score scaling.

Domain-Specific Requirements In some domains (like image pixel intensities), min-max scaling to [0,1] can make intuitive sense. In other domains, subtracting the median and dividing by the interquartile range might better capture the feature’s relevant scale.

Common Normalization Approaches

z-score (Standard Scaling) Often used when you suspect the feature is roughly bell-shaped (Gaussian). The transformation is typically:

Where x is the original value of the feature, mu is the mean of that feature, and sigma is its standard deviation.

Min-Max Scaling Rescales features to a fixed range, usually [0, 1]. It is sensitive to outliers but simple and intuitive for certain data (e.g., pixel intensities). The basic formula is x' = (x - min)/(max - min).

Robust Scaling Subtracts the median and divides by the interquartile range. It is more resilient to outliers. The formula is x' = (x - median)/(IQR).

Log Transform Useful for features that span several orders of magnitude or have a heavily skewed distribution. For example, data that grows exponentially (e.g., population, price) sometimes benefits from a log transform.

Practical Implementation in Python

Below is a small snippet showing how to apply different scalers in scikit-learn:

import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler

# Suppose we have 3 features in our dataset
X = np.array([
    [10,  5000,  1.5 ],
    [12, 10000,  3.0 ],
    [15,  60000,  2.2 ],
    [20,  20000,  0.9 ]
])

# Let's say we use StandardScaler for the first feature, MinMax for the second, and RobustScaler for the third
standard_scaler = StandardScaler()
minmax_scaler    = MinMaxScaler()
robust_scaler    = RobustScaler()

# Scale each column individually
X_col0_scaled = standard_scaler.fit_transform(X[:, [0]])
X_col1_scaled = minmax_scaler.fit_transform(X[:, [1]])
X_col2_scaled = robust_scaler.fit_transform(X[:, [2]])

# Combine them back into one array
X_scaled = np.concatenate((X_col0_scaled, X_col1_scaled, X_col2_scaled), axis=1)
print(X_scaled)

In this example, each column is scaled using a different method. It demonstrates how to address the unique characteristics of each feature.

Potential Pitfalls and Considerations

Increased Complexity Applying a separate scaler to each feature adds a layer of complexity to your data pipeline, which can sometimes make debugging more challenging.

Data Leakage Always remember to fit your scaler (whatever type it is) using only training data and then apply that fitted scaler to the test data. Mixing data from training and testing sets when fitting your scalers can lead to overly optimistic performance estimates.

Consistency for Production When deploying, you must ensure the exact same transformations are applied to new data. Keep track of all transformation parameters (e.g., training means, medians, IQR, etc.).

Correlation Between Features Sometimes correlated features might need to be scaled consistently, or you might need transformations that preserve relationships between features. Using different scalers independently can sometimes break or distort relationships if not done carefully.

Feature Engineering and Domain Knowledge Domain expertise can dictate which transformation is most appropriate. In finance, for example, log-scaling is commonly used on monetary amounts, whereas in image processing, min-max scaling is typical for pixel intensities.

What Could Happen If Different Methods Are Used Improperly?

Misaligned Data Distributions If transformations are chosen arbitrarily and produce distributions that are not well-suited for the model’s assumptions, training might degrade rather than improve.

Potential Overfitting Excessive customization of scaling methods might inadvertently overfit to the training set, particularly if small subsets of features are scaled differently.

Complex Debugging Interpreting results can become harder, especially if something goes wrong in production and you have multiple transformations to trace through.

Are There Cases Where a Single Normalization is Enough?

Yes, if the data is not heavily skewed or if outliers aren’t extreme, a single approach like z-score normalization might be sufficient for all features. However, real-world datasets often contain varied feature distributions, making per-feature scaling choices more attractive.

Follow-up Questions

How do you handle large ranges in data where a standard normalization might not be sufficient?

One strategy is to apply transformations such as logarithmic scaling when dealing with data that spans multiple orders of magnitude (for example, income or population). For heavily skewed data or data with outliers, robust scaling methods based on medians and interquartile ranges can be more effective than z-score normalization.

If we have features that are already in a comparable scale, do we still need normalization?

If the features are already in comparable ranges and have similar scales, then you might not strictly need to apply normalization. However, in many machine learning algorithms, particularly those that rely on distance metrics (like kNN or SVM with RBF kernels), having all features consistently scaled can still improve model performance and convergence behavior.

Are there any risks in applying logarithmic transforms incorrectly?

Yes, log transforms require non-negative input (or at least strictly positive, depending on implementation). If your features can be zero or negative, a direct log transform can lead to errors or undefined values. In such cases, you may need to shift the data to ensure positivity or use alternative transformations (for example, Box-Cox transforms).

Could different scaling methods in an ensemble of models cause problems?

When you combine predictions from multiple models, each model might have different scaling steps. This can complicate the pipeline but generally does not break the ensemble itself, provided that each individual model is consistent with the scaling technique used. Issues arise only if the transformations are mixed incorrectly or not applied consistently to new (test) data.

Do neural network activations or architecture choices influence the normalization method?

Neural networks often benefit from standard or robust scaling of inputs, particularly if the activation functions are sensitive to input magnitude. For instance, widely used activations such as sigmoid or tanh can saturate with very large or very small input values, so scaling helps avoid saturation. However, if you are using architectures like batch normalization or layer normalization, you have built-in mechanisms that reduce the need for external normalization, although carefully scaled inputs can still help.

Summary

Using different normalization techniques on different features is a powerful way to account for varying data distributions, scales, and outliers across features. While it can introduce additional complexity and must be handled carefully, it can significantly improve model performance, stability, and interpretability when done correctly.

Below are additional follow-up questions

Could we automate the selection of scaling methods for each feature?

One way to automate feature-scaling selection is to set up a meta-parameter search. For example, you can try different transformations (z-score, min-max, robust scaling, log transform, etc.) on each feature and select the option that yields the best metric (such as cross-validation accuracy, F1 score, etc.) on a validation set. However, this approach becomes computationally expensive if you have many features and many candidate scaling methods.

When automating the selection process, you must guard against overfitting. If your dataset is not large enough, or if you try too many different transformations, you might pick a scaling method that aligns too precisely with training-set idiosyncrasies. Another risk is data leakage: if you test different scaling methods across your entire dataset (including the test set) before finalizing the choice, your performance estimate can become overly optimistic. This can mislead you into thinking your model generalizes better than it actually does.

Additionally, keep in mind that different features may have highly correlated relationships. A naive, per-feature automated approach might individually pick scaling methods that collectively do not perform well in unison when those features are passed to certain algorithms (especially those that rely on distance metrics or covariance structures). Hence, if you do automate this process, you must treat it like a hyperparameter search—always done strictly on training data with proper validation or cross-validation steps, and repeated checks to ensure robust results.

How do we handle changing data distributions in production systems for which we have already fitted scalers?

In many real-world scenarios, data distributions shift over time (this phenomenon is sometimes referred to as concept drift). If you have already fitted specific scalers during training, your transformations might become suboptimal when new data starts to deviate from the original distribution.

To mitigate this, you can schedule periodic retraining or re-scaling. A common strategy is to accumulate recent data in a rolling window or a buffer and recompute the scaling parameters (e.g., mean, standard deviation, median, interquartile range). However, recalculating the scaler too frequently can lead to instability in your production pipeline if the distribution undergoes only minor or short-lived fluctuations. A balance is needed—some organizations recalculate the scalers monthly or quarterly, for instance.

Another subtle pitfall arises when a major distribution shift occurs—like data from a new demographic group or a different sensor range. In such cases, your old min-max or z-score parameters might no longer capture new extremes. A solution is to apply robust scaling methods which are less sensitive to outliers or to augment the data pipeline with anomaly detection mechanisms that can flag these shifts. For compliance or regulatory environments, you also need to ensure proper versioning of your transformations to maintain reproducibility of results.

What if we need to invert the scaling to return predictions to the original domain?

Often, particularly in regression tasks, you may want your final predictions in the original scale of the target variable (or some of the input features for interpretability purposes). Most libraries (e.g., scikit-learn) provide an inverse_transform method that you can apply after generating predictions.

The main pitfall here is ensuring that you correctly track which scaler was used for the target variable and which were used for the input features. If you accidentally invert using parameters computed for a different feature or a different dataset partition, you will get incorrect results.

If you have used different transformations for different features, you must keep a separate instance of the scaler for each feature, along with the parameters (like mean, standard deviation, or min, max, median, etc.). This can quickly become unwieldy if not well-documented or systematically managed in a pipeline. In a team setting, misalignment of transformations and their inverses can break entire inference pipelines, so versioning and thorough testing are critical to avoid such pitfalls.

When might we use more advanced transformations like Box-Cox or Yeo-Johnson?

Box-Cox and Yeo-Johnson are used primarily for features that violate normality assumptions and have either strictly positive (Box-Cox) or a broader range including negatives (Yeo-Johnson). They aim to find an optimal transformation parameter (lambda) that “normalizes” the distribution as much as possible. This can improve the performance of algorithms that are sensitive to normality assumptions, such as linear or logistic regression, by stabilizing variance and reducing skew.

Pitfalls include the added computational overhead of searching for the best lambda parameter. For large-scale datasets with many features, this can become time-consuming. Furthermore, if the original feature distribution is already close to normal or if normality is not a major concern for your model class (e.g., tree-based methods often handle skew reasonably well), the complexity of Box-Cox or Yeo-Johnson transformations might not be justified. In addition, outliers can sometimes skew the optimal lambda estimate, so you may need robust-lambda search procedures or outlier handling before transformation.

Does the presence of highly imbalanced data or categorical variables complicate normalization?

When dealing with highly imbalanced data, the magnitude of certain features for minority classes might differ substantially from those in the majority class. Normalization per se does not automatically solve class imbalance, but it can interact with it. For instance, if your minority class has extreme values on a certain feature, min-max scaling might squash that variation if the majority class has an even greater range. Alternatively, robust scaling may better preserve distinctions for minority class points if their feature values are outliers in the overall distribution.

Categorical or ordinal variables (e.g., location categories, star ratings) typically do not need numeric scaling. An ordinal feature can sometimes be treated numerically but might need a more careful approach: if you apply min-max scaling to ordinal categories, the distance relationships might become misleading if the numeric labels do not truly represent equidistant intervals. For nominal categorical features, transformations usually involve one-hot encoding (or similar methods). Attempting to directly scale purely categorical data with min-max or z-score transformations is usually meaningless unless you have domain-specific reasons to treat categories numerically.

Can per-feature normalization conflict with dimensionality reduction methods like PCA or TSNE?

Principal Component Analysis (PCA) and other dimensionality reduction methods (e.g., TSNE, UMAP) can be sensitive to the scale of each feature. If one feature has a much larger variance than others, that feature can dominate the principal components. Normalizing features to comparable ranges or standard deviations typically helps PCA capture more meaningful directions of variance.

However, if you apply a unique scaling transformation to each feature without regard to the overall correlation structure, you might distort some relationships. For example, a robust scaler for one feature and a min-max scaler for another could make PCA interpret some directions differently than if you used a single consistent approach. TSNE and UMAP are less about linear variance and more about local neighborhoods, but they are still sensitive to how distance metrics are computed. If your transformations drastically change local distances, you might end up with misleading embeddings.

To handle these nuances, experiment with or validate different scaling strategies before applying PCA or TSNE. Sometimes a universal scaling approach can be simpler and more interpretable. In other cases, especially if certain features are extremely skewed, a careful per-feature transform can enhance the results. The key is consistent cross-validation to compare metrics that matter for your end goal (clustering quality, classification accuracy, interpretability, etc.).

How can we deal with multi-modal distributions under different normalization schemes?

A multi-modal distribution indicates multiple peaks in the data, possibly representing different sub-populations or regimes. Standard transformations like z-score or min-max can distort multi-modal structure, especially if one mode is significantly more populated than others. Log transforms may collapse multiple modes together if the distribution spans wide ranges.

One approach is to separate the data by modes if you have domain knowledge that suggests they are fundamentally different subgroups. You might then normalize each subgroup differently. Alternatively, advanced transformations like mixture models (e.g., Gaussian Mixture Models) can identify these modes, and you can apply a scaling approach per identified cluster. This is more complex but can preserve each cluster’s structure better.

A key pitfall is inadvertently mixing the distributions if you do not accurately identify the underlying modes. In many real-world settings (like text-based features or sensor data across different operating conditions), multi-modality can be subtle. Without domain expertise, you might mistake multiple modes for outliers or random noise. Another subtlety arises if the modes shift over time. A cluster-based transformation that works now might become obsolete if the nature or number of modes changes later.