How does random forest differ from a single decision tree? Why is it less prone to overfitting?

What are the possible ways of improving the accuracy of a linear regression model?

Correlated variables can have zero correlation coeffficient. True or False?

Select the correct statement/s about Ridge Regression.   A. Ridge regression technique prevents coefficients from rising too high. B.  As λ→∞, the impact of the penalty grows, and the ridge regression coefficient estimates will approach infinity.

What is the main disadvantage of decision trees in machine learning?

A company has build a classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong?   Note: Model has successfully deployed and no technical issues are found at client side except the model performance

Why does increasing the complexity of a model reduce bias but increase variance? Provide examples to support your explanation.

What is the main disadvantage of decision trees in machine learning?

After fitting the model with all the required variables______ method is used for identifying and removing independent variables that do not contribute enough to the model.

Select the correct statement/s about Ridge Regression.   A. Ridge regression technique prevents coefficients from rising too high. B.  As λ→∞, the impact of the penalty grows, and the ridge regression coefficient estimates will approach infinity.