The correct answer is: C. logistic regression
Logistic regression is a supervised learning technique that can be used to predict the probability of a binary outcome. It can be used to process both numeric and categorical input attributes.
Linear regression is a supervised learning technique that can be used to predict a continuous outcome. It can only be used to process numeric input attributes.
Bayes classifier is a supervised learning technique that can be used to classify data into two or more classes. It can be used to process both numeric and categorical input attributes.
Backpropagation learning is a supervised learning technique that is used to train artificial neural networks. It can only be used to process numeric input attributes.
Here is a brief explanation of each option:
- Linear regression is a supervised learning technique that can be used to predict a continuous outcome. It is a linear model, which means that it assumes that the relationship between the input variables and the output variable is linear. Linear regression can be used to fit a line to data, and the line can be used to make predictions for new data.
- Bayes classifier is a supervised learning technique that can be used to classify data into two or more classes. It is a probabilistic model, which means that it assigns a probability to each class for each data point. Bayes classifier can be used to make predictions for new data, and the predictions can be used to classify the data.
- Logistic regression is a supervised learning technique that can be used to predict the probability of a binary outcome. It is a linear model, which means that it assumes that the relationship between the input variables and the output variable is linear. Logistic regression can be used to fit a line to data, and the line can be used to make predictions for the probability of the binary outcome.
- Backpropagation learning is a supervised learning technique that is used to train artificial neural networks. It is a backpropagation algorithm, which means that it adjusts the weights of the neural network in a way that minimizes the error between the predicted output and the actual output. Backpropagation learning can be used to train artificial neural networks to perform a variety of tasks, such as classification, regression, and pattern recognition.