MissForest and kNN Imputation for Data Missing at Random

...with a practical demonstration.

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The imputation strategy for missing data largely depends on the type of missingness, which can be of three types:

• Missing completely at random (MCAR): Data is genuinely missing at random and has no relation to any observed or unobserved variables.

Missing completely at random (MCAR)

• Missing at random (MAR): The missingness of one feature can be explained by other observed features in the dataset.

Missing at random (MAR)

• Missing not at random (MNAR): Missingness is either attributed to the missing value itself or the feature(s) that we didn’t collect data for.

Missing not at random (MNAR)

I have observed missing at random (MAR) appearing relatively much more in practical situations than the other two, so today, I want to share two imputation strategies I typically prefer to use in such a case.

I have provided the jupyter notebook for today’s issue towards the end.

Let’s begin!

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#1) kNN imputation

As the name suggests, it imputes missing values using the k-nearest neighbors algorithm.

More specifically, missing features are imputed by running a kNN on non-missing feature values.

The following image depicts how it works:

Visual illustration to kNN Imputer

• Step 1: Select a row (r) with a missing value.

• Step 2: Find its k nearest neighbors using the non-missing feature values.

• Step 3: Impute the missing feature of the row (r) using the corresponding non-missing values of k nearest neighbor rows.

• Step 4: Repeat for all rows with missing values.

Its effectiveness over mean/zero imputation is evident from the demo below.

On the left, we have a feature distribution with missing values. Assuming we have already validated that the data was missing at random (MAR), using mean/zero alters the summary statistics and distribution, as depicted below.

However, as depicted below, kNN imputer appears to be more reliable, and it preserves the summary statistics:

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#2) MissForest

Some major issues with kNN imputation are:

1. It has a high run-time for imputation — especially for high-dimensional datasets.

2. It raises issues with distance calculation in the case of categorical non-missing features.

3. It requires feature scaling, etc.

MissForest imputer is another reliable choice for missing value imputation when data is missing at random (MAR).

As the name suggests, it imputes missing values using the Random Forest algorithm.

The following figure depicts how it works:

Visual illustration of MissForest imputer

• Step 1: To begin, impute the missing feature with a random guess — Mean, Median, etc.

• Step 2: Model the missing feature using Random Forest.

• Step 3: Impute ONLY originally missing values using Random Forest’s prediction.

• Step 4: Back to Step 2. Use the imputed dataset from Step 3 to train the next Random Forest model.

• Step 5: Repeat until convergence (or max iterations).

In case of multiple missing features, the idea (somewhat) stays the same:

MissForest imputer for multiple missing features

• Impute features sequentially in increasing order missingness — features with fewer missing values are imputed first.

Its effectiveness over mean/zero imputation is evident from the demo below.

Like before, on the left, we have a feature distribution with missing values. Again, assuming we have already validated that the data was missing at random (MAR), using mean/zero alters the summary statistics and distribution, as depicted below.

However, as depicted below, MissForest appears more reliable and preserves the summary statistics.

Get started with kNN imputation and MissForest by downloading this Jupyter notebook: kNN imputation and MissForest notebook.

👉 Over to you: What are some other better ways to impute missing values when data is missing at random (MAR)?

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