you are working with an imbalanced dataset, this is an unequal class distribution, and this is reflected in your results. Both models reflect a high precision with the majority class (No) and poor precision with the other class.
Before to compare models is a good idea to balance your dataset, for example resampling your dataset.
Answers
you are working with an imbalanced dataset, this is an unequal class distribution, and this is reflected in your results. Both models reflect a high precision with the majority class (No) and poor precision with the other class.
Before to compare models is a good idea to balance your dataset, for example resampling your dataset.
Best