WorksheetsMachine Learning-1
Total questions: 20
Worksheet time: 10mins
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
In Machine Learning, what is overfitting?
When a model performs well on unseen data but poorly on training data
When a model performs well on training data but poorly on unseen data
When a model performs poorly on training data and unseen data
When a model has too few parameters
When a model is under-trained
Which of these applications relies on Natural Language Processing (NLP), a subset of Machine Learning?
Fraud detection
Self-driving cars
Stock price prediction
Chatbots
Weather forecasting
Which of the following best describes unsupervised learning?
Learning to map inputs to outputs
Learning with labeled data
Finding hidden patterns in unlabeled data
Optimizing a policy for decision-making
Learning based on feedback mechanism
What is the key difference between classification and regression?
Classification predicts categories, while regression predicts continuous values
Classification predicts continuous values, while regression predicts categories
Classification works only with text data
Regression works only with text data
Classification works only with images
Which type of Machine Learning algorithm is used in self-driving cars?
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Self-Supervised Learning
Reinforcement Learning
What is the primary purpose of cross-validation in Machine Learning?
To improve the model's training speed
To evaluate a model's performance on unseen data
To reduce the size of the dataset
To improve feature selection
To reduce Underfitting problem of the model
When using 10-Fold Cross-Validation, how many iterations does the model train on?
10 iterations
5 iterations
15 iterations
9 iterations
20 iterations
What is the main purpose of data preprocessing in Machine Learning?
To reduce the size of the dataset
To increase the size of the dataset
To improve model accuracy by cleaning and transforming raw data
To create more features for the model
To reduce the features before training the model
Which of the following is NOT a step in data preprocessing?
Encoding categorical variables
Data cleaning
Feature scaling
Model training
Data integration from multiple sources
What is feature scaling?
Transforming features to fall within a similar range or scale
The process of creating new features by combining existing ones
Removing irrelevant features from the dataset
Selecting the most important features for the model
