Text Classification and Decision Trees
Text classification involves assigning predefined categories or labels to text documents based on their content. Decision trees are hierarchical tree structures that recursively partition the feature space based on the values of input features. They are particularly well-suited for classification tasks due to their simplicity, interpretability, and ability to handle non-linear relationships.
Decision Trees provide a clear and understandable model for text classification, making them an excellent choice for tasks where interpretability is as important as predictive power. Their inherent simplicity, however, might lead to challenges when dealing with very complex or nuanced text data, leading practitioners to explore more sophisticated or ensemble methods for improvement.
Text Classification using Decision Trees in Python
Text classification is the process of classifying the text documents into predefined categories. In this article, we are going to explore how we can leverage decision trees to classify the textual data.
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