1 1)
What is difference
between R2 and Adjusted R2?
Answer: R2 and
adjusted R2 give you an idea of how many data points fall with in the
line of the regression equation. However, there is one main difference between R2 and the adjusted R2: assumes that every single variable explains the variation in the dependent variable. The R2 adjusted tells you the percentage of variation explained by only the independent variable that actually affect the dependent variable.
line of the regression equation. However, there is one main difference between R2 and the adjusted R2: assumes that every single variable explains the variation in the dependent variable. The R2 adjusted tells you the percentage of variation explained by only the independent variable that actually affect the dependent variable.
1 2)
What is difference
between Liner and logistic Regression?
Answer:
- The Liner regression model’s
data using continuous numeric value and in
logistic regression models the data in the binary
values.
- Liner regression
requires to establish the linear relationship among
dependent and independent variable where it is not necessary for
logistic regression.
- In the linear
regression, the independent variable can be correlated
with each other. On the contrary, in the logistic regression, the variable must
not be correlated with each other.
Q 3) what is difference classification and Regression
Q 3) what is difference classification and Regression
Answer:
- The classification process models a function through which the data is predicted in discrete class labels, on the other hand, regression is the process of creating a model which predict continuous quantity.
- The classification algorithms involve decision tree, logistic regression, etc. In contrast, regression tree (e.g Random forest) and linear regression are the examples of regression algorithms.
- Classification predicts unordered data while regression predicts ordered data.
Regression can be evaluated
using root mean square error. On Contrary, classification is evaluated by
measuring accuracy.- The classification process models a function through which the data is predicted in discrete class labels, on the other hand, regression is the process of creating a model which predict continuous quantity.
- The classification algorithms involve decision tree, logistic regression, etc. In contrast, regression tree (e.g Random forest) and linear regression are the examples of regression algorithms.
- Classification predicts unordered data while regression predicts ordered data.
Q 4) Covariance
and correlation
Answer:
Covariance
and Correlation both primarily assess the relationship between variables. The
closet analogy to the relationship between them is the relationship between the
variance and standard deviation.
Covariance:
measures the total variation of two random variable from
their excepted values. Using the covariance, we can only
measure the direction of relationship (whether the variable tend to move in tandem or show an
inverse relationship). However, it does not indicate the strength of relationship
not dependency between the variable.
Correlation:
measure the strength of the relationship between variable. Correlation is the
scaled measure of covariance. It is dimensionless.