1.What are the conditions for Overfitting and Underfitting?

Ans:
• In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.

• Overfitting – Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.

• Underfitting – High bias and Low Variance. Such a model doesn’t perform well on test data also. For example – Linear Regression is more prone to Underfitting.


2. Which models are more prone to Overfitting?

Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too – this typically happens when there are more features than the number of instances in the training data.


3.  When does feature scaling should be done?

Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.


4. What is a logistic function? What is the range of values of a logistic function?

Ans. f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.


5. What are the drawbacks of a linear model?

Ans. There are a couple of drawbacks of a linear model:

A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model can’t be used for discrete or binary outcomes.
You can’t vary the model flexibility of a linear model.