Term j has good discriminating properties and therefore can be a good feature if it creates an imbalanced distribution of class labels for documents that contain term j:

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BlackTom AI · Accepted Answer

The question asks us to evaluate the statement about a term j having good discriminating properties and therefore potentially being a good feature if it creates an imbalanced distribution of class labels for documents that contain term j.
Option 1 (True): Consider that a feature is valuable for discrimination when its presence (or absence) is strongly associated with particular classes. If term j appears predominantly in documents of one class and rarely in others, the distribution of class labels among documents containing j becomes imbalanced in a way that helps the classifier separate that class from the rest. In text classification, such keyword-level discrimination is exactly what many models exploit; measures like information gain, chi-square, or odds ratios quantify this imbalance and the strength of the association. Therefore, describing this as a condition under which j can be a good feature is reasonable and defensible, as long as the skew reflects genuine class-specific signal rather than noise or sampling bias.
Option 2 (False): The statement would be incorrect if interpreted as “any imbalance guarantees a good feature.” In reality, imbalance alone does not guarantee usefulness: a term that is highly imbalanced but extremely rare may contribute little information in practice or even mislead the model if it heavily reflects the class prior rather than a true discriminative signal. Moreover, global class imbalance can create biased predictions; a feature tied too closely to the majority class may inflate accuracy without improving true discrimination. Effective feature quality depends on the strength and reliability of the association between term presence and class, not merely the existence of imbalance. Therefore, claiming that imbalance alone makes a term a good discriminative feature overlooks these nuances and is misleading.

In summary, the true statement aligns with the intuition that discriminative features often exhibit class-specific distributions, but it is important to ensure that the imbalance reflects a real, robust signal rather than noise or dataset biases. The false view would be too absolute, ignoring cases where imbalance is not informative or harms generalization.

SP2025.B69.DAT.562.24 COMPLETE Quiz #3 in Module 3 for VIDEO 2: Feature Selection: Gini Impurity

Term j has good discriminating properties and therefore can be a good feature if it creates an imbalanced distribution of class labels for documents that contain term j:

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