How to Fix: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT

The ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT error occurs when the L-BFGS algorithm fails to converge during optimization, indicating that the model is unable to find a suitable solution.

This issue affects all users who have defined prediction pipelines with the specified configuration and are using versions of scikit-learn or pandas that support L-BFGS optimization.

🔍 Why This Happens

• The primary reason for this error is due to the choice of optimization algorithm. L-BFGS is a popular optimization algorithm, but it may not be suitable for all datasets. In particular, it can struggle with datasets containing both numeric and categorical data.
• An alternative reason for this error is that the dataset contains a large number of categorical variables, which can lead to slow convergence or failure of the L-BFGS algorithm.

🔧 Proven Troubleshooting Steps

✨ Wrapping Up

To resolve this issue, try adjusting the optimization algorithm or experimenting with different hyperparameters. Additionally, ensure that all categorical variables are encoded correctly and consider scaling or normalizing numeric variables. By taking these steps, you can improve the stability and convergence of your model and achieve better results.