Final answer:
The false statement about supervised/unsupervised data analysis is that data is not labeled for supervised analysis. Supervised analysis involves labeled data for model training, whereas unsupervised analysis works with unlabeled data to find patterns.
Step-by-step explanation:
C. Data is not labeled for supervised The statement that is false about supervised/unsupervised data analysis is: C. Data is not labeled for supervised analysis. In supervised data analysis, the data is indeed labeled, which means that each example in the training dataset is paired with the correct output. The goal in supervised learning is to develop a model that can make predictions based on these labels.
On the other hand, unsupervised data analysis involves data without predefined labels, where the goal is to discover underlying patterns or groupings in the data.In supervised data analysis, the data is labeled. The goal is to train a model to predict or classify future data based on the labeled data. Examples of supervised analysis include classification and regression tasks. On the other hand, in unsupervised data analysis, the data is not labeled. The goal is to discover patterns or relationships in the data without any specific prediction or classification tasks in mind. Clustering and dimension reduction are common tasks in unsupervised analysis.