The correct answer is A. True.
Linear regression is a supervised machine learning algorithm that is used to predict the value of a dependent variable (y) based on the values of one or more independent variables (x). The goal of linear regression is to find a linear relationship between the independent and dependent variables that minimizes the sum of the squared errors between the predicted values and the actual values.
Linear regression is a very common machine learning algorithm and is used in a wide variety of applications, including forecasting, classification, and clustering. It is a relatively simple algorithm to understand and implement, and it can be used to solve a variety of problems.
Here is a brief explanation of each option:
- Option A: True. Linear regression is a supervised machine learning algorithm. This means that it is trained on a set of data that includes both the independent and dependent variables. The algorithm then uses this data to learn a linear relationship between the variables. This relationship can then be used to predict the value of the dependent variable for new data.
- Option B: False. Linear regression is not an unsupervised machine learning algorithm. Unsupervised machine learning algorithms do not require labeled data. Instead, they learn from unlabeled data by finding patterns and relationships in the data.
I hope this explanation is helpful. Please let me know if you have any other questions.