Machine learning techniques differ from statistical techniques in that machine learning methods

Solution(By Examveda Team)

Machine learning techniques and statistical techniques are related fields, but they have distinct differences in their approaches and characteristics.

Option A: typically assume an underlying distribution for the data.
In statistical techniques, it is common to assume specific probability distributions for the data, and many statistical methods are based on these assumptions. In contrast, machine learning methods often do not make strong assumptions about the underlying data distribution. Instead, they focus on learning patterns and relationships directly from the data.

Option B: are better able to deal with missing and noisy data.
Machine learning methods often have techniques and algorithms designed to handle missing and noisy data effectively. They can adapt to imperfect data and still make predictions or classifications, whereas statistical methods may struggle with data quality issues.

Option C: are not able to explain their behavior.
This statement is not entirely accurate. Machine learning methods can be interpretable to some extent, and efforts have been made to develop explainable AI techniques. While some complex machine learning models may be less interpretable than traditional statistical models, they are not inherently incapable of explaining their behavior.

Option D: have trouble with large-sized datasets.
Machine learning methods are often well-suited for large-sized datasets, and many machine learning algorithms can scale to handle massive amounts of data. In fact, they are frequently used in big data analytics and large-scale applications.

In summary, the key differences between machine learning and statistical techniques lie in their approaches to data assumptions, handling missing/noisy data, and explainability. Therefore, the correct answer is Option A: typically assume an underlying distribution for the data.