Q.No.4. _________ refers to the property of some datasets where no straight line can perfectlyseparate different classes.

The term that refers to the property of some datasets where no straight line can perfectly separate different classes is called non-linearly separable. In machine learning and statistics, this means that the data points belonging to different classes cannot be divided by a linear boundary (a straight line) in the feature space.

For example, consider a simple two-dimensional dataset where one class forms a circle and another class forms a surrounding ring. No straight line can separate these classes; thus, the dataset is considered non-linearly separable. In such cases, more complex models such as kernel methods or neural networks are often applied to effectively classify the data by transforming it into higher-dimensional spaces where a linear separation might be feasible.