Which of the following best describes a confusion matrix? Multiple Choice It is a table summarizing the prediction results. It has as many rows and columns as classifications to predict. It can be used to calculate other performance metrics. All of these choices are correct. None of these choices are correct. Which of the following best describes supervised learning? Multiple Choice The training data contain missing labels or incomplete data. The training data match inputs to nodes in the network. The training data contain input-output pairs. The training data only include input values. None of these choices are correct. Which of the following best describes semi-supervised learning? Multiple Choice The training data contain some missing labels or incomplete data. The training data match inputs to nodes in the network. The training data contain input-output pairs. The training data only include input values. None of these choices are correct. Which of the following best describes reinforcement learning? Multiple Choice The model determines how elements of the dataset are alike. The training data match inputs to nodes in the network. The training data contain input-output pairs. The model learns by trial-and-error. None of these choices are correct. Which of the following best describes a confusion matrix? Multiple Choice It is a table summarizing the prediction results. It has as many rows and columns as classifications to predict. It can be used to calculate other performance metrics. All of these choices are correct. None of these choices are correct. Which of the following best describes unsupervised learning? Multiple Choice The training data contain missing labels or incomplete data. The training data match inputs to nodes in the network. The training data contain input-output pairs. The training data only include input values. None of these choices are correct