produce sparse matrices of real numbers that can be fed into any machine learning model.

1. dictvectorizer

2.featurehasher

3.both a & b

4.None of the mentioned

dataset with many features contains information proportional to the independence of all features and their variance.

1.normalized

2.unnormalized

3. both a & b

4.None of the mentioned

allows exploiting the natural sparsity of data while extracting principal components.

1.sparsepca

2.kernelpca

3. svd

4. init parameter

How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

1.1

2.2

3.cant say

4.None of These

In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the .

1.concuttent matrix

2.convergance matrix

3.supportive matrix

4.covariance matrix

In reinforcement learning if feedback is negative one it is defined as .

1.penalty

2.overlearning

3.reward

4.None of the above

provides some built-in datasets that can be used for testing purposes.

1.scikit-learn

2.classification

3.regression

4.None of the above

The SVMs are less effective when:

1.the data is linearly separable

2.the data is clean and ready to use

3. the data is noisy and contains overlapping points

4.None of These

What does learning exactly mean?

1.robots are programed so that they can perform the task based on data they gather from sensors.

2.a set of data is used to discover the potentially predictive relationship.

3.learning is the ability to change according to external stimuli and remembering most of all previous experiences.

4. it is a set of data is used to discover the potentially predictive relationship.

What is Model Selection in Machine Learning?

1.the process of selecting models among different mathematical models, which are used to describe the same data set

2.when a statistical model describes random error or noise instead of underlying relationship

3.find interesting directions in data and find novel observations/ database cleaning

4. all above

Which of the following is characteristic of best machine learning method ?

1.fast

2.accuracy

3.scalable

4.all above

Which of the following is true about Residuals ?

1.lower is better

2.higher is better

3.a or b depend on the situation

4.none of these

Which of the following is true about Ridge or Lasso regression methods in case of feature selection?

1.ridge regression uses subset selection of features

2.lasso regression uses subset selection of features

3.both use subset selection of features

4.None of the above

Which of the one is true about Heteroskedasticity?

1. linear regression with varying error terms

2. linear regression with constant error terms

3.linear regression with zero error terms D.

4.none of these

A supervised scenario is characterized by the concept of a .

1.programmer

2.teacher

3.author

4.farmer

According to , its a key success factor for the survival and evolution of all species.

1.claude shannons theory

2.gini index

3.darwins theory

4.none of above

Common deep learning applications / problems can also be solved using

1.real-time visual object identification

2.classic approaches

3.automatic labeling

4.bio-inspired adaptive systems

Even if there are no actual supervisors learning is also based on feedback provided by the environment

1.supervised

2.reinforcement

3.unsupervised

4.None of the above

Features being classified is of each other in Nave Bayes Classifier

1.independent

2.dependent

3.partial dependent

4.None of These

Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values.

1.mean

2.variance

3.discrete

4.random

Gaussian Nave Bayes Classifier is distribution

1.continuous

2.discrete

3.binary

4.None of These

How do you handle missing or corrupted data in a dataset?

1.a. drop missing rows or columns

2.replace missing values with mean/median/mode

3.assign a unique category to missing values

4.all of the above

If there is only a discrete number of possible outcomes called

1.modelfree

2.categories

3.prediction

4.none of above

In a linear regression problem, we are using R-squared to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model. Which of the following option is true?

1.if r squared increases, this variable is significant.

2.if r squared decreases, this variable is not significant.

3. individually r squared cannot tell about variable importance. we cant say anything about it right now.

4.none of these

Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

1.you will always have test error zero

2.you can not have test error zero

3.Both 1 and 2

4.none of the above

Multinomial Nave Bayes Classifier is distribution

1. continuous

2.discrete

3.binary

4.None of these

Naive Bayes classifiers are a collection------------------of algorithms

1.classification

2.clustering

3.regression

4.All of the above

Naive Bayes classifiers is Learning

1.supervised

2.unsupervised

3.both

4.none

overlearning causes due to an excessive .

1.capacity

2.regression

3.reinforcement

4.accuracy

scikit-learn also provides a class for per- sample normalization,

1.normalizer

2.imputer

3.classifier

4.All of the above

scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency

1. labelencoder

2.labelbinarizer

3.dictvectorizer

4.imputer

Some people are using the term instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.

1.inference

2.interference

3.accuracy

4.None of the above

Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very small C (C~0)?

1.misclassification would happen

2.data will be correctly classified

3. cant say

4.none of these

Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

1.since the there is a relationship means our model is not good

2.since the there is a relationship means our model is good

3.cant say

4.none of these

Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

1. since the there is a relationship means our model is not good

2.since the there is a relationship means our model is good

3.cant say

4.None of These

SVM is a algorithm

1.classification

2.clustering

3.regression

4.All of the above

SVM is a learning

1.supervised

2.unsupervised

3.both

4.None of These

Techniques involve the usage of both labeled and unlabeled data is called .

1.supervised

2.semi-supervised

3.unsupervised

4.none of the above

The parameter can assume different values which determine how the data matrix is initially processed

1.run

2.start

3.init

4.stop

The term can be freely used, but with the same meaning adopted in physics or system theory.

1.accuracy

2.cluster

3.regression

4.prediction

To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?

1.scatter plot

2.barchart

3.histograms

4. none of these

What are the different Algorithm techniques in Machine Learning?

1.supervised learning and semi-supervised learning

2.unsupervised learning and transduction

3.both a & b

4.None of the mentioned

What is the standard approach to supervised learning?

1. split the set of example into the training set and the test

2.group the set of example into the training set and the test

3.a set of observed instances tries to induce a general rule

4. learns programs from data

What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function

1.1

2.2

3.1 and 2

4.None of these

When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .

1.overfitting

2.overlearning

3.classification

4.regression

Which are two techniques of Machine Learning ?

1.genetic programming and inductive learning

2.speech recognition and regression

3.both a & b

4.none of the mentioned

Which of the following are several models

1.regression

2.classification

3.both (a) and (b)

4.None of the above

Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free

1.1,2 and 3.

2.1,3 and 4

3.1 and 3.

4.All of the above

Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

1.auc-roc

2.accuracy

3.logloss

4.mean-squared-error

Which of the following function provides unsupervised prediction ?

1.cl_forecastb

2.cl_nowcastc

3.cl_precastd

4.None of the mentioned

Which of the following is an example of a deterministic algorithm?

1.pca

2.k-means

3.both (a) and (b)

4.None of the above

Which of the following is not Machine Learning?

1.artificial intelligence

2.rule based inference

3.both a & b

4.None of the mentioned

Which of the following is true about Residuals ?

1.lower is better

2.higher is better

3.a or b depend on the situation

4.none of these

Which of the following model model include a backwards elimination feature selection routine?

1.mcv

2.mars

3.mcrs

4.All of the above

Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.

1.minmaxscaler

2.maxabsscaler

3.both a & b

4.None of the mentioned

Which of the following statement is true about outliers in Linear regression?

1.linear regression is sensitive to outliers

2.linear regression is not sensitive to outliers

3.cant say

4.None of These

Which of the following statement is true about outliers in Linear regression?

1.linear regression is sensitive to outliers

2.linear regression is not sensitive to outliers

3.cant say

4.none of these

Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-

1.1 and 2

2.1 and 3

3.2 and 4

4.None of the above

which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.

1. the polynomial degree

2.whether we learn the weights by matrix inversion or gradient descent

3.the use of a constant-term

4.None of These

While using all labels are turned into sequential numbers.

1.labelencoder class

2.labelbinarizer class

3.dictvectorizer

4.featurehasher

- Machine Learning (ML) MCQ Set 01
- Machine Learning (ML) MCQ Set 02
- Machine Learning (ML) MCQ Set 03
- Machine Learning (ML) MCQ Set 04
- Machine Learning (ML) MCQ Set 05
- Machine Learning (ML) MCQ Set 06
- Machine Learning (ML) MCQ Set 07
- Machine Learning (ML) MCQ Set 08
- Machine Learning (ML) MCQ Set 09
- Machine Learning (ML) MCQ Set 10

Online Exam TestTop Tutorials are Core Java,Hibernate ,Spring,Sturts.The content on Online Exam Testwebsite is done by expert team not only with the help of books but along with the strong professional knowledge in all context like coding,designing, marketing,etc!