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WorksheetsIntroduction to Machine Learning
Total questions: 20
Worksheet time: 10mins
A computer program is said to learn from __________ with
respect to some class of ______ and ________, if its performance at tasks in T, as measured by P, improves with E.
Experience, Performance,Tasks
Tasks, Performance, Experience
Experience, Tasks, Performance
Performance, Tasks, Experience
__________ has been used to train vehicles to steer correctly
and autonomously on road.
Machine learning
Data mining
Neural networks
Robotics
Which type of learning algorithm used for "future crude oil prices "?
Recognizing similarities
predicting
creatind patterns
all the above
Which of the following is not type of learning?
Semi-unsupervised Learning
Unsupervised Learning
Supervised Learning
Reinforcement Learning
What would make a robot intelligent?
It responds to the environment.
It responds to the environment according to previous experiences.
It calculates mathematical problems faster than human minds.
It can jump 1.5 meters higher than humans.
supervised learning algorithm deals with..
labelled and unlabelled data
unlabelled data
labelled data
None
Semi-supervised learning algorithm deals with..
labelled and unlabelled data
unlabelled data
labelled data
None
Unsupervised learning algorithm deals with..
labelled and unlabelled data
unlabelled data
labelled data
None
To categorize the given test example, which type of learning algorithms are used?
regression
clasification
supervised learning algorithm
All the above
In__________ decision-making agent that takes actions in an environment and receives reward (or penalty) for its actions in trying to solve a problem
Regression
classification
supervised learning
Reinforcement learning
___________ is a type of supervised learning where a target feature, which is of categorical type.
Regression
Labelling
Classification
None
Which of the folowing algorithm is a lazy learner?
K medoids
Decision Tree
K means clustering
K-NN Algorithm
from confusion matrix,Accuracy is defined as P+N(X). Then X=?
TP+PN
TP+FP
P
N
In decision tree, an attribute is selected as node by calculating the _________ of the attribute
Information gain
Entropy
feature values
Information and Gain
_________is the task of approximating a mapping function (f) from input variables (X) to a continuous output variable (Y).
Classification
Regression
Clustering
Decision Tree
_____ refers to a broad set of techniques for finding subgroups in a data set on the basis of the characteristics of the objects within that data set.
Grouping
Segmenting
Clustering
Partitioning
A ___________ is a function that best describes the target in supervised machine learning.
hypothesis space
Bias
variance
hypothesis
_______ occurs when we try to approximate a complex or complicated relationship with a much simpler model.
Variance
Bias
Bias and variance
overfitting
The models with high bias tends to ________
low test error
low training error
overfitting
underfitting
Because of low bias and high variance , we get _____ model
high error
perfectly fitting
underfitting
over fitting
