Which of the following steps assumption in regression modeling impacts the trade off between under fitting and Overtingting the most?

Which of the following step / assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most. Choosing the right degree of polynomial plays a critical role in fit of regression. If we choose higher degree of polynomial, chances of overfit increase significantly.

Why minimum square error function which is the cost function of linear regression is not suitable for logistic regression?

Gradient descent requires convex cost functions. Mean Squared Error, commonly used for linear regression models, isn’t convex for logistic regression. This is because the logistic function isn’t always convex. The logarithm of the likelihood function is however always convex.

How do you improve regression performance?

Here are several options:

1. Add interaction terms to model how two or more independent variables together impact the target variable.
2. Add polynomial terms to model the nonlinear relationship between an independent variable and the target variable.
3. Add spines to approximate piecewise linear models.

How does a linear regression work?

Linear Regression is the process of finding a line that best fits the data points available on the plot, so that we can use it to predict output values for inputs that are not present in the data set we have, with the belief that those outputs would fall on the line.

What happens when we introduce more variables to a linear regression model?

Adding more independent variables or predictors to a regression model tends to increase the R-squared value, which tempts makers of the model to add even more variables. This is called overfitting and can return an unwarranted high R-squared value.

Why linear regression is important?

Why linear regression is important Linear-regression models have become a proven way to scientifically and reliably predict the future. Because linear regression is a long-established statistical procedure, the properties of linear-regression models are well understood and can be trained very quickly.