We Tried 5 Missing Data Imputation Methods: The Simplest Method Won (Sort Of)

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# The Setup

 
You're about to train a model when you notice 20% of your values are missing. Do you drop those rows? Fill them in with averages? Use something fancier? The answer matters more than you'd think.

If you Google it, you'll find dozens of imputation methods, from the dead-simple (just use the mean) to the sophisticated (iterative machine learning models). You might think that fancy methods are better. KNN considers similar rows. MICE builds predictive models. They must outperform just slapping on the average, right?

We thought so too. We were wrong.

# The Experiment

 
We grabbed the Crop Recommendation dataset from StrataScratch projects - 2,200 soil samples across 22 crop types, with features such as nitrogen levels, temperature, humidity, and rainfall. A Random Forest hits 99.6% accuracy on this thing. It's almost suspiciously clean.

This analysis extends our Agricultural Data Analysis project, which explores the same dataset through EDA and statistical testing. Here, we ask: what happens when clean data meets a real-world problem - missing values?

Perfect for our experiment.

We introduced 20% missing values (completely at random, simulating sensor failures), then tested five imputation methods:

Our testing was thorough; we used 10-fold cross-validation across five random seeds (a total of 50 runs per method). To ensure that no information from the test set leaked into the training set, our imputation models were trained on the training sets only. For our statistical tests, we applied the Bonferroni correction. We also normalized the input features for both KNN and MICE, as if we did not normalize them, an input with values ranging between 0 and 300 (rainfall) would have a much greater impact than an input with a range of 3 to 10 (pH) when performing the distance calculation for these methods. Full code and reproducible results are available in our notebook.

Then we ran it and stared at the results.

# The Surprise

 
Here's what we expected: KNN or MICE would win, because they're smarter. They consider relationships between features. They use actual machine learning.

Here's what we got:

The Median and Mean are tied for first place. The sophisticated methods came in third and fourth.

We ran the statistical test. Mean vs. Median: p = 0.7. Not even close to significant. They're effectively identical.

But here's the kicker: both of them significantly outperformed KNN and MICE (p < 0.001 after Bonferroni correction). The simple methods didn't just match the fancy ones. They beat them.  

# Wait, What?

 
Before you throw out your MICE installation, let's dig into why this happened.

The task was prediction. We measured accuracy. Does the model still classify crops correctly after imputation? For that specific goal, what matters is preserving the predictive signal, not necessarily the exact values.

Mean imputation does something interesting: it replaces missing values with a "neutral" value that doesn't push the model toward any particular class. It's boring, but it's safe. The Random Forest can still find its decision boundaries.

KNN and MICE try harder; they estimate what the actual value might have been. But in doing so, they can introduce noise. If the nearest neighbors aren't that similar, or if MICE's iterative modeling picks up spurious patterns, you might be adding error rather than removing it.

The baseline was already high. At 99.6% accuracy, this is a pretty easy classification problem. When the signal is strong, imputation errors matter less. The model can afford some noise.

Random Forest is robust. Tree-based models handle imperfect data well. A linear model struggled more with the variance distortion of mean imputation.

Not so fast.

# The Plot Twist

 
We measured something else: correlation preservation.

Here's the thing about real data: features don't exist in isolation. They move together. In our dataset, when soil has high Phosphorus, it usually has high Potassium as well (correlation of 0.74). This isn't random; farmers typically add these nutrients together, and certain soil types retain both similarly.

When you impute missing values, you may accidentally break these relationships. Mean imputation fills in "average Potassium" regardless of what Phosphorus looks like in that row. Do that enough times, and the connection between P and K starts to fade. Your imputed data might look fine column-by-column, but the relationships between columns are quietly falling apart.

Why does this matter? If your next step is clustering, PCA, or any analysis where feature relationships are the point, you're working with damaged data and don't even know it.

We checked: after imputation, how much of that P↔K correlation survived?

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The rankings completely flipped.

KNN preserved the correlation almost perfectly. Mean and Median destroyed about a quarter of it. And Random Sample (which samples values independently for each column) eliminated the relationship.

This makes sense. Mean imputation replaces missing values with the same number regardless of what the other features look like. If a row has high Nitrogen, Mean doesn't care; it still imputes the average Potassium. KNN looks at similar rows, so if high-N rows tend to have high-K, it'll impute a high-K value.

# The Trade-Off

 
Here's the real finding: there is no single best imputation method. Instead, select the most appropriate method based on your specific goal and context.

The accuracy rankings and correlation rankings are nearly opposite:

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(At least the Random Sample is consistent - it's bad at everything.)

This trade-off isn't unique to our dataset. It's baked into how these methods work. Mean/Median are univariate, and they look at one column at a time. KNN/MICE are multivariate, and they consider relationships. Univariate methods preserve marginal distributions but destroy correlation. Multivariate methods preserve structure and can produce some form of predictive error/noise.

# So, What Should You Actually Do?

 
After running this experiment and digging through the literature, here's our practical guide:

Use Mean or Median when:

• Your goal is prediction (classification, regression)
• You're using a robust model (Random Forest, XGBoost, neural nets)
• Missing rate is under 30%
• You need something fast

Use KNN when:

• You need to preserve feature relationships
• Downstream task is clustering, PCA, or visualization
• You want correlations to survive for exploratory analysis

Use MICE when:

• You need valid standard errors (for statistical inference)
• You're reporting confidence intervals or p-values
• The missing data mechanism might be MAR (Missing at Random)

Avoid Random Sample:

• It's tempting because it "preserves the distribution"
• But it destroys all multivariate structure
• We couldn't find a good use case

# The Honest Caveats

 
We tested one dataset, one missing rate (20%), one mechanism (MCAR), and one downstream model (Random Forest). Your setup may vary. The literature shows that on other datasets, MissForest and MICE often perform better. Our finding that simple methods compete is real, but it's not universal.

# The Bottom Line

 
We went into this experiment expecting to confirm that sophisticated imputation methods are worth the complexity. Instead, we found that for prediction accuracy, the humble mean held its own, while completely failing at preserving the relationships between features.

The lesson isn't "always use mean imputation." It's "know what you're optimizing for."

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If you just need predictions, start simple. Test whether KNN or MICE actually helps on your data. Don't assume they will.

If you need the correlation structure for downstream analysis, Mean will silently wreck it while giving you perfectly reasonable accuracy numbers. That's a trap.

And whatever you do, scale your features before using KNN. Trust us on this one.
 
 

Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.