Machine Learning-1

What is a labeled dataset used in?

• Unsupervised Learning

• Supervised Learning

• Reinforcement Learning

• Semi-Supervised Learning

• Self-Supervised Learning

Identify the Problem:

We have Information of all previous general elections with respect to party, votes, Candidates, Locality, Issues in nation, etc. With All this information we need to find the winner of next general election.

• Classification

• Regression

• Clustering

• Association

Identify the problem:

We have information of all houses in a particular area like size, type and design of house, locality, color, previous purchase etc. On the basis of these data we have to find the price of a particular house.

• Classification

• Regression

• Clustering

• Probabilistic

Identify the problem:

You have details of corona affected positive cases found in various regions. Depending upon parameters like number of increasing corona infected patients, weather conditions etc.. Regions are classified into red zone, Green zone and Orange Zone. On the basis of these data we have to predict the region of a city.

• Classification

• Regression

• Clustering

• Association

Which of the following is not a method of cross validation?

• Leave One Out CV

• K-Fold CV

• Stratified K-Fold CV

• Timeline CV

If I am using all features of my dataset and I achieve 100% accuracy on my training set, but 70% on validation set, what might be the reason?

• Overfitting

• Underfitting

• Bestfitting

• Average fitting

Select prime reason for using unsupervised Machine Learning Algorithms.

• Finds all kind of unknown patterns in data.

• Help you to solve maze kind of problems

• Help you to Predict whether a fruit is apple or not

• Help you to predict prize of stock

What type of learning uses both labeled and unlabeled data?

• Reinforcement Learning

• Unsupervised Learning

• Semi-Supervised Learning

• Supervised Learning

Which of the following techniques is used to prevent overfitting in a model?

• Data Augmentation

• All the mentioned

• Feature Selection

• Cross Validation

What is the primary goal of feature selection in machine learning?

• To improve data quality

• To reduce overfitting

• To enhance model interpretability

• To increase the model complexity