WorksheetsPage 1
Total questions: 25
Worksheet time: 13mins
Machine Learning is a subset of
Data Mining
Artificial Intelligence
Computer Networks
Operating Systems
Which of the following is NOT a type of Machine Learning?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Predictive Learning
In supervised learning, the training data contains
Only input data
Only output data
Input–output pairs
Random data
Which algorithm is used for classification problems?
Linear Regression
Logistic Regression
K-Means
Apriori
The main goal of Machine Learning is to
Store data
Program computers manually
Enable systems to learn from data
Design hardware
Which of the following is an example of supervised learning?
Market basket analysis
Spam email detection
Clustering customers
Dimensionality reduction
The term “training data” refers to
Data used to test a model
Data used to build a model
Unlabeled data
Noise in data
Which of the following is an unsupervised learning algorithm?
Decision Tree
Naïve Bayes
K-Means
Logistic Regression
A dataset with labeled output is used in
Supervised learning
Unsupervised learning
Reinforcement learning
Clustering
Which language is commonly used for Machine Learning?
HTML
Python
CSS
XML
Which algorithm works on the principle of Bayes theorem?
SVM
Decision Tree
Naïve Bayes
KNN
Overfitting occurs when
Model performs well on test data
Model is too simple
Model performs well on training data but poorly on test data
Dataset is small
Which metric is commonly used for classification accuracy?
Mean Squared Error
Confusion Matrix
KNN algorithm is based on
Distance measure
Probability theory
Entropy
Gradient descent
Which of the following is NOT a distance metric?
Euclidean distance
Manhattan distance
Cosine similarity
Entropy
Decision Tree splitting is based on
Accuracy
Information Gain
Mean
Variance only
Which learning method uses reward and penalty?
Supervised learning
Unsupervised learning
Reinforcement learning
Semi-supervised learning
In Linear Regression, the relationship between variables is
Non-linear
Exponential
Linear
Random
Which technique is used to reduce overfitting?
Increasing model complexity
Regularization
Removing training data
Increasing epochs infinitely
Which algorithm is sensitive to outliers?
K-Means
Decision Tree
Random Forest
Naïve Bayes
Bias–Variance tradeoff deals with
Data imbalance
Model complexity and generalization
Training speed
Feature scaling
Entropy in Decision Trees measures
Distance
Purity of data
Accuracy
Variance
Which kernel is commonly used in SVM?
Linear
Polynomial
RBF
All of the above
PCA is mainly used for
Classification
Clustering
Dimensionality reduction
Regression
Gradient Descent is used to
Increase error
Minimize cost function
Maximize entropy
Split nodes
