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730+ Machine Learning (ML) Solved MCQs

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable a system to improve its performance on a specific task over time. In other words, machine learning algorithms are designed to allow a computer to learn from data, without being explicitly programmed.

These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .

351.

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

A. penalty
B. overlearning
C. reward
D. none of above
Answer» A. penalty
352.

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

A. claude shannon\s theory
B. gini index
C. darwin's theory
D. none of above
Answer» C. darwin's theory
353.

A supervised scenario is characterized by the concept of a          .

A. programmer
B. teacher
C. author
D. farmer
Answer» B. teacher
354.

overlearning causes due to an excessive            .

A. capacity
B. regression
C. reinforcement
D. accuracy
Answer» A. capacity
355.

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

A. pca
B. k-means
C. none of the above
Answer» A. pca
356.

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

A. mcv
B. mars
C. mcrs
D. all above
Answer» B. mars
357.

Can we extract knowledge without apply feature selection

A. yes
B. no
Answer» A. yes
358.

While using feature selection on the data, is the number of features decreases.

A. no
B. yes
Answer» B. yes
359.

Which of the following are several models

A. regression
B. classification
C. none of the above
Answer» C. none of the above
360.

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

A. scikit-learn
B. classification
C. regression
D. none of the above
Answer» A. scikit-learn
361.

While using           all labels are turned into sequential numbers.

A. labelencoder class
B. labelbinarizer class
C. dictvectorizer
D. featurehasher
Answer» A. labelencoder class
362.

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

A. dictvectorizer
B. featurehasher
C. both a & b
D. none of the mentioned
Answer» C. both a & b
363.

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

A. labelencoder
B. labelbinarizer
C. dictvectorizer
D. imputer
Answer» D. imputer
364.

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

A. minmaxscaler
B. maxabsscaler
C. both a & b
D. none of the mentioned
Answer» C. both a & b
365.

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

A. normalizer
B. imputer
C. classifier
D. all above
Answer» A. normalizer
366.

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

A. normalized
B. unnormalized
C. both a & b
D. none of the mentioned
Answer» B. unnormalized
367.

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

A. concuttent matrix
B. convergance matrix
C. supportive matrix
D. covariance matrix
Answer» D. covariance matrix
368.

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

A. run
B. start
C. init
D. stop
Answer» C. init
369.

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

A. sparsepca
B. kernelpca
C. svd
D. init parameter
Answer» A. sparsepca
370.

Which of the following is true about Residuals ?

A. lower is better
B. higher is better
C. a or b depend on the situation
D. none of these
Answer» A. lower is better
371.

Overfitting is more likely when you have huge amount of data to train?

A. true
B. false
Answer» B. false
372.

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?

A. since the there is a relationship means our model is not good
B. since the there is a relationship means our model is good
C. can�t say
D. none of these
Answer» A. since the there is a relationship means our model is not good
373.

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

A. you will always have test error zero
B. you can not have test error zero
C. none of the above
Answer» C. none of the above
374.

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?

A. if r squared increases, this variable is significant.
B. if r squared decreases, this variable is not significant.
C. individually r squared cannot tell about variable importance. we can't say anything about it right now.
D. none of these.
Answer» C. individually r squared cannot tell about variable importance. we can't say anything about it right now.
375.

Which of the one is true about Heteroskedasticity?

A. linear regression with varying error terms
B. linear regression with constant error terms
C. linear regression with zero error terms
D. none of these
Answer» A. linear regression with varying error terms
376.

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

A. 1,2 and 3.
B. 1,3 and 4.
C. 1 and 3.
D. all of above.
Answer» D. all of above.
377.

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

A. scatter plot
B. barchart
C. histograms
D. none of these
Answer» A. scatter plot
378.

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

A. the polynomial degree
B. whether we learn the weights by matrix inversion or gradient descent
C. the use of a constant-term
Answer» A. the polynomial degree
379.

Can we calculate the skewness of variables based on mean and median?

A. true
B. false
Answer» B. false
380.

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

A. ridge regression uses subset selection of features
B. lasso regression uses subset selection of features
C. both use subset selection of features
D. none of above
Answer» B. lasso regression uses subset selection of features
381.

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-

A. 1 and 2
B. 1 and 3
C. 2 and 4
D. none of the above
Answer» A. 1 and 2
382.

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

A. 1
B. 2
C. can't say
Answer» B. 2
383.

Conditional probability is a measure of the probability of an event given that another event has already occurred.

A. true
B. false
Answer» A. true
384.

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

A. 1
B. 2
C. 1 and 2
D. none of these
Answer» C. 1 and 2
385.

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)?

A. misclassification would happen
B. data will be correctly classified
C. can't say
D. none of these
Answer» A. misclassification would happen
386.

The cost parameter in the SVM means:

A. the number of cross- validations to be made
B. the kernel to be used
C. the tradeoff between misclassification and simplicity of the model
D. none of the above
Answer» C. the tradeoff between misclassification and simplicity of the model
387.

If you remove the non-red circled points from the data, the decision boundary will

A. true
B. false
Answer» B. false
388.

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

A. drop missing rows or columns
B. replace missing values with mean/median/mode
C. assign a unique category to missing values
D. all of the above
Answer» D. all of the above
389.

The SVMs are less effective when:

A. the data is linearly separable
B. the data is clean and ready to use
C. the data is noisy and contains overlapping points
Answer» C. the data is noisy and contains overlapping points
390.

If there is only a discrete number of possible outcomes called          .

A. modelfree
B. categories
C. prediction
D. none of above
Answer» B. categories
391.

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

A. inference
B. interference
C. accuracy
D. none of above
Answer» A. inference
392.

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

A. accuracy
B. cluster
C. regression
D. prediction
Answer» D. prediction
393.

Common deep learning applications / problems can also be solved using        

A. real-time visual object identification
B. classic approaches
C. automatic labeling
D. bio-inspired adaptive systems
Answer» B. classic approaches
394.

Identify the various approaches for machine learning.

A. concept vs classification learning
B. symbolic vs statistical learning
C. inductive vs analytical learning
D. all above
Answer» D. all above
395.

what is the function of Unsupervised Learning?

A. find clusters of the data and find low-dimensional representations of the data
B. find interesting directions in data and find novel observations/ database cleaning
C. interesting coordinates and correlations
D. all
Answer» D. all
396.

What are the two methods used for the calibration in Supervised Learning?

A. platt calibration and isotonic regression
B. statistics and informal retrieval
Answer» A. platt calibration and isotonic regression
397.

Which of the following are several models for feature extraction

A. regression
B. classification
C. none of the above
Answer» C. none of the above
398.

Let's say, a Linear regression model perfectly fits the training data (train error

A. you will always have test error zero
B. you can not have test error zero
C. none of the above
Answer» C. none of the above
399.

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

A. 1,2 and 3.
B. 1,3 and 4.
C. 1 and 3.
D. all of above.
Answer» D. all of above.
400.

Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?

A. it is more likely for x1 to be excluded from the model
B. it is more likely for x1 to be included in the model
C. can't say
D. none of these
Answer» B. it is more likely for x1 to be included in the model

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