Which of the following is a better process for building a text classification model?

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BlackTom AI · Accepted Answer

When building a text classification model, there are logical steps that should be followed in a typical machine learning pipeline.
Option 1: 'Select features -> Train model -> Evaluate model' omits the crucial step of feature extraction, which is necessary to convert raw text into numerical representations that a model can use. Without extracting features, there is nothing meaningful to train on.
Option 2: 'Select features -> Extract features -> Train model -> Evaluate model' places feature selection before feature extraction. Since you first need to have a set of features extracted from the text, selecting features beforehand is not feasible or meaningful; feature selection typically happens after feature extraction when you want to reduce dimensionality.
Option 3: 'Extract features -> Select features -> Train model -> Evaluate model' follows a sensible order: you convert text to features (e.g., via TF-IDF, embeddings, etc.), then (optionally) reduce or select the most informative features, then train the classifier, and finally evaluate its performance on held-out data.
Option 4: 'Extract features -> Train model -> Evaluate model' misses the step of feature selection. It may still work in simple cases, but without selecting features, you may retain too many or noisy features, which can hurt generalization and training efficiency. A feature selection or dimensionality reduction step is commonly used to improve performance and interpretability.
In summary, the sequence that includes both extracting features and then selecting features, followed by training and evaluation, aligns with standard practice for text classification pipelines, whereas the other options omit or misplace essential steps.

SP2025.B69.DAT.562.24 COMPLETE Quiz #1 in Module 3 for VIDEO 0: Text Classification - Overview

Which of the following is a better process for building a text classification model?

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