zero_division` parameter

 

zero_division` parameter

UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

 UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.

  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

 UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

 UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.

  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

 UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.

  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

 UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.

  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))

              precision    recall  f1-score   support

           0       0.00      0.00      0.00       2.0

           1       0.00      0.00      0.00       0.0

    accuracy                           0.00       2.0

   macro avg       0.00      0.00      0.00       2.0

weighted avg       0.00      0.00      0.00       2.0

Solution Explaination:

These warnings appear when:

• Precision Warning: A class has no predicted samples (i.e., the model did not predict any instance as belonging to a particular class). In such cases, precision for that class is undefined (since division by zero would occur).
• Recall Warning: A class has no true samples (i.e., there are no actual samples in the data belonging to a particular class). Here, recall for that class is undefined.

Why is This Happening?

There are several possible reasons for these warnings:

1. Imbalanced Dataset: If your dataset has a class imbalance, some classes may have very few or no samples, causing the metrics to be undefined for those classes.
2. Model's Inability to Predict Certain Classes: The model might not be able to predict certain classes at all, especially if the data does not provide enough distinguishing features for those classes.
3. Incorrect Data Processing or Feature Engineering: If certain features or tokens were removed (e.g., through aggressive stop word removal), it might affect the model's ability to correctly classify certain samples.

How to Address the Warnings

Here are a few strategies to mitigate these warnings:

1. Use the zero_division Parameter: In Scikit-Learn, you can control the behavior when division by zero occurs in precision and recall calculations by using the zero_division parameter.

Setting zero_division=0 will set precision or recall to 0.0 when there are no predicted samples or true samples, respectively.

Analyze and Balance Your Dataset: If you have a highly imbalanced dataset, consider balancing it using techniques like oversampling the minority class, undersampling the majority class, or using more sophisticated methods like SMOTE (Synthetic Minority Over-sampling Technique).

Improve Feature Engineering: Ensure that your feature engineering steps (such as text processing in NLP tasks) are not too aggressive, causing the removal of significant data that the model needs to make predictions.

Evaluate Model Performance with Micro or Macro Averages:

• Macro-Average: Computes metrics independently for each class and then takes the average. This treats all classes equally.
• Micro-Average: Aggregates contributions of all classes to compute the average metric. This is preferable when you want to give equal importance to each sample.