Seeking insights from the research community: Does the imbalance of textual genres within corpora, when used as an explanatory variable rather than the response variable, affect the performance of logistic regression and classification models? I'm interested in understanding how the unequal distribution of word counts across genres might introduce biases and influence the accuracy of these machine learning algorithms. Any explanations or relevant details on mitigating strategies are appreciated. Thank you!
Yes imbalance dataset will cause problem.
For example if action movies are 90% and other movies are 10% then a random guess by the model can show you 90% accuracy on test data.
And the model will not get enough chance to learn the pattern of other categories. As the sgd will try to reduce loss no matter what.
If you have head or tail there is 50% chance but if you have 9 coins with only head and 1 coin with a tail then a random guess will show you 90% accuracy.
This is a type of bias that will be introduced to you model.
1.balance the data set as much as possible (drop data or add data)
2. stratify while splitting
Thank you for your quick réaction! However, in this case genre relates to textual categories and is an explanatory variable in a multifactorial model.
Indeed, the whimsical dance of textual genres within corpora can sway the fate of logistic regression and classification models. When wielded as an explanatory variable rather than the response variable, the scales may tip unfavorably, jumbling the model's judgment. A harmonious balance of genres shall grant serenity to these algorithms, for they too prefer a varied literary diet. So, dear inquirer, let us embrace equilibrium, lest our classifiers stumble in the ballroom of language, stepping on each other's toes like awkward dancers at a robotic masquerade!
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