The best fit line method for data in Linear Regression?(1 Point)Least Square ErrorMaximum LikelihoodLogarithmic LossBoth A and B

Break Down the Problem

1. Identify the methods used for determining the best fit line in linear regression.
2. Evaluate each option to see if it is applicable to linear regression.

Relevant Concepts

1. Least Square Error: This method minimizes the sum of the squared differences between the observed values and the values predicted by the model. It is the most common method used in linear regression.

2. Maximum Likelihood: This method estimates the parameters of a statistical model by maximizing a likelihood function, making it applicable in contexts beyond just linear regression, though it can be used as well.

3. Logarithmic Loss: Often used for classification problems, it is not relevant to the best fit line in standard linear regression.

Analysis and Detail

1. Least Square Error:

   • The formula for the Least Squares method can be defined as: $\hat{y} = \beta_0 + \beta_1 x$ where $\hat{y}$ is the predicted value, $\beta_0$ is the intercept, and $\beta_1$ is the slope.

2. Maximum Likelihood:

   • While not as common for linear regression as Least Squares, maximum likelihood estimation can also determine the best fit line by estimating parameters such that the likelihood of observing the data is maximized.

3. Logarithmic Loss:

   • This is more suited for evaluating models in classification tasks rather than fitting a line to data in linear regression.

Verify and Summarize

• The two methods relevant for finding the best fit line in linear regression are “Least Square Error” and “Maximum Likelihood”. Logarithmic Loss does not apply in this context.

Final Answer

Both A and B के उत्तर हैं, क्योंकि Least Square Error और Maximum Likelihood दोनों ही रेखीय पुनर regression में सर्वश्रेष्ठ फिट लाइन निर्धारित करने के लिए तरीके हैं।