MissForest: A Better Alternative To Zero (or Mean) Imputation

Replacing (imputing) missing values with mean or zero or any other fixed value:

• alters summary statistics

• changes the distribution

• inflates the presence of a specific value

This can lead to:

• inaccurate modeling

• incorrect conclusions, and more.

Instead, always try to impute missing values with more precision.

In one of the earlier posts, we discussed kNN imputer. Today’s post builds on that by addressing its limitations, which are:

1. High run-time for imputation — especially for high-dimensional datasets.

2. Issues with distance calculation in case of categorical non-missing features.

3. Requires feature scaling, etc.

MissForest imputer is another reliable choice for missing value imputation when your 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:

• 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:

• 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 image below.

• Mean/Zero alters the summary statistics and distribution.

• MissForest imputer preserves them.

What’s more, MissForest can impute even if the data has categorical non-missing features.

MissForest is based on Random Forest, so one can impute from categorical and continuous data.

Get started with MissForest imputer: MissingPy MissForest.

👉 Over to you: What are some other better ways to impute missing values?

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