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# Configuration
You are about to train the model when you notice that 20% of the values are missing. Do you delete these lines? Fill them with mediums? Utilize something fancier? The answer matters more than you think.
If you google it, you’ll find dozens of imputation methods, ranging from the extremely straightforward (just exploit an average) to the sophisticated (iterative machine learning models). You might think that fancy methods are better. KNN includes similar lines. MICE builds predictive models. They must be performing better than the average tap, right?
We thought so too. We were wrong.
# Experiment
We caught it Crop recommendation dataset from StrataScratch Projects – 2,200 soil samples from 22 crops, with characteristics such as nitrogen levels, temperature, humidity and precipitation. Random Forest achieves 99.6% accuracy in this case. It’s almost suspiciously pristine.
This analysis extends ours Agricultural data analysis a project that examines the same dataset using EDA and statistical testing. Here we ask: what happens when pristine data encounters a real problem – missing values?
Perfect for our experiment.
We introduced 20% of the missing values (completely randomly, simulating sensor failures) and then tested five imputation methods:
Our tests were thorough; we applied 10-fold cross-validation on five random seeds (total of 50 runs per method). To ensure that no information from the test set leaks into the training set, our imputation models were trained only on the training sets. We used the Bonferroni correction in our statistical tests. We also normalized the input features for both KNN and MICE, as if we did not normalize them, inputs with values ranging from 0 to 300 (rainfall) would have a much greater impact than inputs ranging from 3 to 10 (pH) when calculating distances for these methods. Full code and reproducible results are available in our notebook.
We then checked and viewed the results.
# Surprise
Here’s what we expected: KNN or MICE will win because they are smarter. They consider relationships between features. They exploit real machine learning.
Here’s what we got:
The median and mean rank first. Sophisticated methods came in third and fourth place.
We performed a statistical test. Average vs. median: p = 0.7. Not even close to significant. They are actually identical.
But here’s the problem: both of them performed significantly better than KNN and MICE (p < 0.001 after Bonferroni correction). Straightforward methods did not fit only fancy ones. They beat them.
# Wait, what?
Before you throw away your MICE installation, let’s understand why this happened.
The task was to predict. We measured the accuracy. After imputation, does the model still classify crops correctly? For this particular purpose, what matters is maintaining the predictive signal, not necessarily the exact values.
Mean imputation does something captivating: it replaces missing values with a “neutral” value that does not shift the model to any particular class. It’s dull but safe and sound. Random Forest can still find its decision limits.
KNN and MICE are trying harder; they estimate what the actual value might have been. However, this way they can introduce noise. If the nearest neighbors are not very similar, or if MICE iterative modeling detects spurious patterns, you may be adding error instead of removing it.
The baseline was already high. With an accuracy of 99.6%, this is a fairly effortless classification problem. When the signal is forceful, imputation errors are less crucial. The model can afford to make some noise.
Random forest it is solid. Tree-based models cope well with imperfect data. The linear model had more difficulty distorting the mean imputation variance.
Not so rapid.
# Plot twist
We measured something else: correlation behavior.
Here’s the truth about real data: features don’t exist in isolation. They move together. Our data set shows that when soil is high in phosphorus, it tends to also be high in potassium (0.74 correlation). This is not accidental; farmers typically add these ingredients together, and some soil types retain both similarly.
By assigning missing values, you may accidentally break these relationships. The average imputation fills in the “average potassium” regardless of what the phosphorus looks like in that row. Do this enough times and the connection between P and K will begin to fade. Your assigned data may look fine column by column, but the relationships between the columns are silently falling apart.
Why does this matter? If your next step is grouping, PCAor any analysis where relationships between functions are the most crucial, you are working with corrupted data and you don’t even know it.
We checked: how much of this P↔K correlation survived after imputation?
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The rankings have completely flipped.
KNN preserved this correlation almost perfectly. The Mean and Median destroyed about a quarter. Random sampling (which samples values independently for each column) eliminated this relationship.
That makes sense. Mean assignment replaces missing values with the same number, regardless of what the other features look like. If there is a lot of nitrogen in the row, the Medium doesn’t care; still assigns average potassium levels. KNN checks for similar rows, so if high N rows tend to have a high K value, it will assign a high K value.
# Compromise
Here’s the real bottom line: there is no single best imputation method. Instead, choose the most appropriate method based on your specific goal and context.
Accuracy rankings and correlation rankings are almost opposite:
Photo by the author
(At least the random sample is consistent – it’s bad at everything.)
This trade-off is not unique to our dataset. This is deeply rooted in how these methods work. Mean/median are univariate and consider one column at a time. KNN/MICE are multidimensional and take into account relationships. Univariate methods preserve marginal distributions but destroy correlation. Multivariate methods retain structure and may generate some form of predictive error/noise.
# So what should you actually do?
After conducting this experiment and reviewing the literature, here is our practical guide:
Utilize the mean or median when:
- Your goal is prediction (classification, regression)
- You exploit a strong model (Random Forest, XGBoost, neural networks)
- Missing rate is below 30%
- You need something rapid
Utilize KNN when:
- Relationships between functions must be maintained
- The next task is clustering, PCA or visualization
- You want the correlations to survive for exploratory analysis
Utilize MICE when:
- You need valid standard errors (for statistical inference)
- You provide confidence intervals or p-values
- The missing data mechanism may be MAR (missing at random)
Avoid random sample:
- It’s tempting because it “preserves distribution”
- But this destroys the entire multidimensional structure
- We didn’t find a good exploit case
# Candid reservations
We tested one dataset, one missing value rate (20%), one mechanism (Mcare) and one further model (Random Forest). Your configuration may vary. The literature shows that for other datasets Mrs.Forest and MICE often work better. Our finding that straightforward methods compete with each other is real but not universal.
# Conclusion
We entered this experiment expecting confirmation that sophisticated imputation methods were worth their complexity. Instead, we found that when it came to prediction accuracy, a modest average held, with no relationship between features at all.
The lesson is not “always use mean imputation.” It’s “you know what you’re optimizing for.”
Photo by the author
If you just need predictions, start straightforward. Check if KNN or MICE actually helps with your data. Don’t assume they will.
If you need a correlation structure for further analysis, Mean will silently destroy it while still providing completely reasonable accuracy numbers. It’s a trap.
Whatever you do, scale your features before using KNN. Trust us on this one.
Nate Rosidi is a data scientist and product strategist. He is also an adjunct professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for job interviews using real interview questions from top companies. Nate writes about the latest career trends, gives interview advice, shares data science projects, and discusses all things SQL.