The Hughes phenomenon claims that for a fixed size dataset, a machine learning model performs worse as dimensionality risesTRUEFALSE

Step 1: Understand the question The question is asking whether the Hughes phenomenon claims that as dimensionality increases, a machine learning model performs worse for a fixed size dataset. We need to determine if this statement is true or false.

Step 2: Analyze the statement The Hughes phenomenon refers to a concept in machine learning where increasing the dimensionality of a dataset can lead to a decrease in model performance. The statement claims that as dimensionality rises, a machine learning model performs worse for a fixed size dataset.

Step 3: Evaluate the statement To evaluate the statement, we need to consider the Hughes phenomenon. According to the Hughes phenomenon, as the number of features or dimensions in a dataset increases, the model may struggle to find meaningful patterns or relationships. This can lead to overfitting, where the model becomes too specialized to the training data and performs poorly on new, unseen data.

Step 4: Determine the answer Based on the understanding of the Hughes phenomenon, we can conclude that the statement is TRUE. As dimensionality rises, a machine learning model can indeed perform worse for a fixed size dataset.

Step 5: Provide the answer The answer to the question is TRUE. The Hughes phenomenon claims that for a fixed size dataset, a machine learning model performs worse as dimensionality rises.