Stratified sampling is better in case of classification problems because it takes into account the balance of classes in train and test sets. The proportion of classes is maintained and hence the model performs better. In case of random sampling of data, the data is divided into two parts without taking into consideration the balance classes in the train and test sets. Hence some classes might be present only in tarin sets or validation sets. Hence the results of the resulting model are poor in this case.

The choice of sampling technique in machine learning, particularly for classification models, depends on the characteristics of your dataset and the nature of the problem you are trying to solve. Two common types of sampling are:

Stratified Sampling:
- Explanation: In stratified sampling, the dataset is divided into different strata or groups based on the target variable. The sampling is then performed independently within each stratum.
- Advantages:
  - Ensures that each class or category in the target variable is represented in the training and testing sets.
  - Helps prevent the imbalance of classes, which is crucial in classification problems where one class significantly outnumbers the others.
- Use Case: Stratified sampling is particularly beneficial when dealing with imbalanced datasets, where some classes are underrepresented.
Random Sampling:
- Explanation: In random sampling, observations are randomly selected from the entire dataset without considering the class distribution.
- Advantages:
  - Simplicity and ease of implementation.
  - Works well when the dataset is already balanced or if class imbalance is not a significant concern.
- Use Case: Random sampling can be suitable for well-balanced datasets or when the class distribution is not a critical factor in the problem.

Correct Answer: The choice between stratified and random sampling depends on the characteristics of the dataset. If the dataset has imbalanced classes, stratified sampling is generally preferred to ensure that each class is adequately represented in both the training and testing sets. On the other hand, if the dataset is balanced, random sampling might be sufficient and simpler to implement.

In summary, the decision should be based on the specific needs of the classification problem and the distribution of classes in the dataset.