Fix Input contains NaN during model training

An estimator rejects a feature matrix containing missing values. Replacing every missing value with zero makes the code run but can create a misleading relationship.

Why this happens

Estimator support for missing values differs, and missingness can itself carry information. A zero may be a real observed value rather than an appropriate substitute.

Step-by-step solution

1. Measure missingness by feature and investigate its origin. Distinguish an absent measurement from an invalid parsing result.

2. Select a suitable imputation policy and fit it only on training data. Put the imputer inside the pipeline used for cross-validation.

3. Consider a missingness indicator where useful, and define a policy for columns that are entirely missing. Validate the policy against the business meaning of each feature.

Worked example

from sklearn.impute import SimpleImputer
import numpy as np
imputer = SimpleImputer(strategy='median')
clean = imputer.fit_transform([[1.0],[np.nan],[3.0]])
assert clean[:,0].tolist() == [1.0,2.0,3.0]

Verify the fix

The missing example receives the training median of 2. Test production records with missing values and confirm that the already fitted imputer is reused.

Prevent a repeat

Monitor missingness rates alongside model quality. A sudden increase may indicate an upstream system failure rather than a need for a different algorithm.

Reference

Official documentation or standards guidance

An original Gistmax troubleshooting guide. Scenarios and sample values are illustrative; configuration depends on your software version and environment. Cover diagram: Gistmax Tutorials.