Generative AI Certificate Q&A: Greatest challenges with supervised learning binary classification?

Question

What is one of the greatest challenges with supervised learning binary classification?

A. You need a lot of pre-classified or labeled data for the training set.
B. These systems are complex and inherently unreliable.
C. You have to come up with multiple classifications.
D. You have to let the machine come up with its own classification labels.

Answer

A. You need a lot of pre-classified or labeled data for the training set.

Explanation 5

The correct answer to the question “What is one of the greatest challenges with supervised learning binary classification?” is A. You need a lot of pre-classified or labeled data for the training set.

Supervised learning binary classification is a type of machine learning technique where the goal is to classify input data into one of two possible classes. This approach requires a labeled dataset, which consists of input data paired with corresponding labels indicating their respective classes. The model learns from this labeled data to make predictions on new, unseen examples.

The availability of a substantial amount of pre-classified or labeled data is crucial for the success of supervised learning binary classification. Here are some key reasons why this is a significant challenge:

• Data Collection and Annotation: Acquiring a large amount of labeled data can be time-consuming and expensive. It often requires human experts to manually label the data, which can be a tedious and labor-intensive task. For certain domains or specific classes, it may be even more challenging to gather sufficient labeled examples.
• Labeling Bias and Quality: The quality of the labeled data is critical to the performance of the classifier. Human annotators may introduce unintentional biases, leading to imbalanced or inaccurate labels. Ensuring consistency and accuracy in labeling across different annotators is a challenging task.
• Generalization and Robustness: Supervised models heavily rely on the assumption that the labeled data is representative of the entire distribution of the problem domain. However, in practice, it can be difficult to collect a truly diverse and comprehensive dataset that covers all possible scenarios and variations. Consequently, models trained on limited data may struggle to generalize well to unseen examples or perform poorly in edge cases.
• Data Sparsity and Data Imbalance: In some applications, certain classes may be underrepresented in the dataset (data imbalance), or the available labeled data might be sparse for certain classes. Imbalanced or sparse datasets can negatively impact the model’s ability to learn and classify accurately. Addressing such challenges often requires specific techniques like data augmentation, oversampling, undersampling, or more advanced approaches tailored to handle imbalanced data.

While the other options listed in the question may present their own challenges in different contexts, the need for a substantial amount of pre-classified or labeled data is widely recognized as one of the primary challenges in supervised learning binary classification.

Reference

• Binary classification – Wikipedia
• 5 Classification Algorithms for Machine Learning | Built In
• Binary Classification – LearnDataSci
• Learning from positive and unlabeled data: a survey | SpringerLink
• A Supervised Learning Algorithm to Binary Classification Problem for Spiking Neural Networks | IEEE Conference Publication | IEEE Xplore

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