Data Science Interview Questions- I

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.

                    

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

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.

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.