MODULE 2 S3.2.2

Question 1

Decision trees can grow many branches until each split is as __________ as possible.

Answer 1

pure

Question 2

____________________ are a popular model that mitigates this problem of overfitting in decision trees.

Answer 2

Random Forests

Question 3

Alternatives to Using a Decision Tree

Answer 3

Random forest
Gradient boosting machine
Support vector machine
Neural network

Question 4

It is a popular machine learning algorithm that merges the outputs of numerous decision trees to produce a single outcome.

It is also suitable for both classification and regression tasks.

Answer 4

Random Forest

Question 5

Random Forest’s strength lies in its ability to handle ________________ and mitigate ______________

Answer 5

complex dataset
overfitting

Question 6

Random forest technique

Answer 6

Bagging

Question 7

It is a method where multiple machine learning models are trained to solve the same problem and then combined to improve the final output.

Answer 7

Ensemble learning

Question 8

It is a collection of models is used to make predictions rather than an individual model.

Answer 8

Ensemble learning

Question 9

Two types of ensemble methods/techniques

Answer 9

Bagging
Boosting

Question 10

Ensemble Process

Bagging : _______________
Boosting : _______________

Answer 10

Parallel
Sequential

Question 11

Bagging is known as _____________

Answer 11

Bootstrap Aggregation

Question 12

It serves as the ensemble technique in the Random Forest algorithm. It is a method of generating a new dataset with a replacement from an existing dataset.

Answer 12

Bagging

Question 13

How does bagging work?

Answer 13

1. Sampling
2. Independent training
3. Aggregation

Question 14

Prediction (Bagging Aggregation)

Classification : ______________
Regression : _______________

Answer 14

majority voting
average

Question 15

Strengths of Bagging

Answer 15

Reduces Variance
Parallelism

Question 16

Another ensemble technique, but unlike Bagging, it focuses on reducing bias by sequentially training models.

Answer 16

Boosting

Question 17

T/F In boosting, the models created are independent.

Answer 17

False

Question 18

T/F In boosting, the models are not independent; they are built one after another, with each model trying to fix the mistakes of the previous ones.

Answer 18

True

Question 19

It refers to the family of an algorithm that converts weak learners (base learners) to strong learners.

Answer 19

Boosting

Question 20

classifiers that are correct only up to a small extent with the actual classification

Answer 20

weak learner

Question 21

classifiers that are well correlated with the actual classification

Answer 21

strong learner

Question 22

How does boosting work?

Answer 22

1. Sequential training
2. Weight adjustment
3. Final model

Question 23

Strengths of Boosting

Answer 23

Reduces Bias
Higher Accuracy

Question 24

Two types of hyperparameters of random forest

Answer 24

Increase the Predictive Power
Increase the Speed

Question 25

Predictive power parameters

Answer 25

n_estimators
max_features
mini_sample_leaf
criterion
max_leaf nodes

Question 26

Speed parameters

Answer 26

n_jobs
random_state
oob_score