Improved text classification models
Although the above framework can be applied to a number of text classification problems, to achieve good precision some improvements can be made to the general framework. As an example, Here are some tips to boost the performance of text classification models and this framework.
1. Text cleaning: Text cleaning can help reduce noise present in text data in the form of stop words, punctuation marks, suffix variations, etc. This post can help you understand how to put text classification into practice in detail.
2. Hstacking Text Features / NLP with text function vectors: In the feature engineering section, we generate a series of vectors with different characteristics, and their combination can help improve the accuracy of the classifier.
3. Hyperparameter adjustment in modeling: Ajustar los parametersThe "parameters" are variables or criteria that are used to define, measure or evaluate a phenomenon or system. In various fields such as statistics, Computer Science and Scientific Research, Parameters are critical to establishing norms and standards that guide data analysis and interpretation. Their proper selection and handling are crucial to obtain accurate and relevant results in any study or project.... es un paso importante, a series of parameters such as the length of the tree, leaves, network parameters, etc. can be adjusted for a better fit model.
4. Assembly models: Stacking different models and combining their results can help to further improve the results.. Read more about set models here.
