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.
201.

This  clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration Select one:

A. k-means clustering
B. conceptual clustering
C. expectation maximization
D. agglomerative clustering
Answer» A. k-means clustering
202.

Which one of the following is the main reason for pruning a Decision Tree?

A. to save computing time during testing
B. to save space for storing the decision tree
C. to make the training set error smaller
D. to avoid overfitting the training set
Answer» D. to avoid overfitting the training set
203.

You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?

A. your decision trees are too shallow.
B. you need to increase the learning rate.
C. you are overfitting.
D. incorrect data
Answer» A. your decision trees are too shallow.
204.

The K-means algorithm:

A. requires the dimension of the feature space to be no bigger than the number of samples
B. has the smallest value of the objective function when k = 1
C. minimizes the within class variance for a given number of clusters
D. converges to the global optimum if and only if the initial means are chosen as some of the samples themselves
Answer» C. minimizes the within class variance for a given number of clusters
205.

Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering?
1. Single-link
2. Complete-link
3. Average-link

A. 1 and 2
B. 1 and 3
C. 2 and 3
D. 1, 2 and 3
Answer» D. 1, 2 and 3
206.

In which of the following cases will K-Means clustering fail to give good results?
1. Data points with outliers
2. Data points with different densities
3. Data points with round shapes
4. Data points with non-convex shapes

A. 1 and 2
B. 2 and 3
C. 2 and 4
D. 1, 2 and 4
Answer» D. 1, 2 and 4
207.

Hierarchical clustering is slower than non-hierarchical clustering?

A. true
B. false
C. depends on data
D. cannot say
Answer» A. true
208.

High entropy means that the partitions in classification are

A. pure
B. not pure
C. useful
D. useless
Answer» B. not pure
209.

Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?

A. decision trees
B. density-based clustering
C. model-based clustering
D. k-means clustering
Answer» B. density-based clustering
210.

The main disadvantage of maximum likelihood methods is that they are _____

A. mathematically less folded
B. mathematically less complex
C. mathematically less complex
D. computationally intense
Answer» D. computationally intense
211.

The maximum likelihood method can be used to explore relationships among more diverse sequences, conditions that are not well handled by maximum parsimony methods.

A. true
B. false
C. -
D. -
Answer» A. true
212.

Which Statement is not true statement.

A. k-means clustering is a linear clustering algorithm.
B. k-means clustering aims to partition n observations into k clusters
C. k-nearest neighbor is same as k-means
D. k-means is sensitive to outlier
Answer» C. k-nearest neighbor is same as k-means
213.

what is Feature scaling done before applying K-Mean algorithm?

A. in distance calculation it will give the same weights for all features
B. you always get the same clusters. if you use or don\t use feature scaling
C. in manhattan distance it is an important step but in euclidian it is not
D. none of these
Answer» A. in distance calculation it will give the same weights for all features
214.

With Bayes theorem the probability of hypothesis H¾ specified by P(H) ¾ is referred to as

A. a conditional probability
B. an a priori probability
C. a bidirectional probability
D. a posterior probability
Answer» B. an a priori probability
215.

The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%.
We also know that 3% of the adult population subscribes to automotive magazine.
The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%.
Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car

A. 0.0398
B. 0.0389
C. 0.0368
D. 0.0396
Answer» D. 0.0396
216.

What is the naïve assumption in a Naïve Bayes Classifier.

A. all the classes are independent of each other
B. all the features of a class are independent of each other
C. the most probable feature for a class is the most important feature to be cinsidered for classification
D. all the features of a class are conditionally dependent on each other
Answer» D. all the features of a class are conditionally dependent on each other
217.

Based on survey , it was found that the probability that person like to watch serials is 0.25 and the probability that person like to watch netflix series is 0.43. Also the probability that person like to watch serials and netflix sereis is 0.12. what is the probability that a person doesn't like to watch either?

A. 0.32
B. 0.2
C. 0.44
D. 0.56
Answer» A. 0.32
218.

What is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model?

A. p
B. 2p
C. p(p+1)/2
D. p(p+3)/2
Answer» D. p(p+3)/2
219.

Give the correct Answer for following statements.
1. It is important to perform feature normalization before using the Gaussian kernel.
2. The maximum value of the Gaussian kernel is 1.

A. 1 is true, 2 is false
B. 1 is false, 2 is true
C. 1 is true, 2 is true
D. 1 is false, 2 is false
Answer» C. 1 is true, 2 is true
220.

Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?

A. negative log-likelihood
B. log-liklihood
C. cross entropy
D. residual sum of square
Answer» A. negative log-likelihood
221.

Consider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively.

A. 0
B. 1
C. 0.1
D. 0.9
Answer» B. 1
222.

Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that Select one:

A. y is false when x is known to be false.
B. y is true when x is known to be true.
C. x is true when y is known to be true
D. x is false when y is known to be false.
Answer» B. y is true when x is known to be true.
223.

Which of the following statements about Naive Bayes is incorrect?

A. attributes are equally important.
B. attributes are statistically dependent of one another given the class value.
C. attributes are statistically independent of one another given the class value.
D. attributes can be nominal or numeric
Answer» B. attributes are statistically dependent of one another given the class value.
224.

How the entries in the full joint probability distribution can be calculated?

A. using variables
B. using information
C. both using variables & information
D. none of the mentioned
Answer» B. using information
225.

How many terms are required for building a bayes model?

A. 1
B. 2
C. 3
D. 4
Answer» C. 3
226.

Skewness of Normal distribution is ___________

A. negative
B. positive
C. 0
D. undefined
Answer» C. 0
227.

The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?

A. the attributes are not linearly related.
B. as the value of one attribute increases the value of the second attribute also increases
C. as the value of one attribute decreases the value of the second attribute increases
D. the attributes show a linear relationship
Answer» C. as the value of one attribute decreases the value of the second attribute increases
228.

8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters,
C1, C2, C3 has following observations:
C1: {(2,2), (4,4), (6,6)}
C2: {(0,4), (4,0),(2,5)}
C3: {(5,5), (9,9)}
What will be the cluster centroids if you want to proceed for second iteration?

A. c1: (4,4), c2: (2,2), c3: (7,7)
B. c1: (6,6), c2: (4,4), c3: (9,9)
C. c1: (2,2), c2: (0,0), c3: (5,5)
D. c1: (4,4), c2: (3,3), c3: (7,7)
Answer» D. c1: (4,4), c2: (3,3), c3: (7,7)
229.

In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?

A. p(x/c)
B. p(c/x)
C. p(c)
D. p(x)
Answer» A. p(x/c)
230.

Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance
2. Generalized models
3. Better interpretability

A. 1 and 3
B. 2 and 3
C. 1, 2 and 3
D. 1 and 2
Answer» D. 1 and 2
231.

What is back propagation?

A. it is another name given to the curvy function in the perceptron
B. it is the transmission of error back through the network to adjust the inputs
C. it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
D. none of the mentioned
Answer» A. it is another name given to the curvy function in the perceptron
232.

Which of the following is an application of NN (Neural Network)?

A. sales forecasting
B. data validation
C. risk management
D. all of the mentioned
Answer» D. all of the mentioned
233.

Neural Networks are complex ______________ with many parameters.

A. linear functions
B. nonlinear functions
C. discrete functions
D. exponential functions
Answer» A. linear functions
234.

Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

A. true – this works always, and these multiple perceptrons learn to classify even complex problems
B. false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
C. true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded
D. false – just having a single perceptron is enough
Answer» C. true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded
235.

Which one of the following is not a major strength of the neural network approach?

A. neural network learning algorithms are guaranteed to converge to an optimal solution
B. neural networks work well with datasets containing noisy data
C. neural networks can be used for both supervised learning and unsupervised clustering
D. neural networks can be used for applications that require a time element to be included in the data
Answer» A. neural network learning algorithms are guaranteed to converge to an optimal solution
236.

The network that involves backward links from output to the input and hidden layers is called

A. self organizing maps
B. perceptrons
C. recurrent neural network
D. multi layered perceptron
Answer» C. recurrent neural network
237.

Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms?
1. Max number of samples
2. Max features
3. Bootstrapping of samples
4. Bootstrapping of features

A. 1
B. 2
C. 3&4
D. 1,2,3&4
Answer» D. 1,2,3&4
238.

What is back propagation?
a) It is another name given to the curvy function in the perceptron
b) It is the transmission of error back through the network to adjust the inputs
c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
d) None of the mentioned

A. a
B. b
C. c
D. b&c
Answer» C. c
239.

In an election for the head of college, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes.which of the following ensembles method works similar to the discussed elction Procedure?

A. ??bagging
B. boosting
C. stacking
D. randomization
Answer» A. ??bagging
240.

What is the sequence of the following tasks in a perceptron?
Initialize weights of perceptron randomly
Go to the next batch of dataset
If the prediction does not match the output, change the weights
For a sample input, compute an output

A. 1, 4, 3, 2
B. 3, 1, 2, 4
C. 4, 3, 2, 1
D. 1, 2, 3, 4
Answer» A. 1, 4, 3, 2
241.

In which neural net architecture, does weight sharing occur?

A. recurrent neural network
B. convolutional neural network
C. . fully connected neural network
D. both a and b
Answer» D. both a and b
242.

Which of the following are correct statement(s) about stacking?
1. A machine learning model is trained on predictions of multiple machine learning models
2. A Logistic regression will definitely work better in the second stage as compared to other classification methods
3. First stage models are trained on full / partial feature space of training data

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. 1,2 and 3
Answer» C. 1 and 3
243.

 Given above is a description of a neural network. When does a neural network model become a deep learning model?

A. when you add more hidden layers and increase depth of neural network
B. when there is higher dimensionality of data
C. when the problem is an image recognition problem
D. when there is lower dimensionality of data
Answer» A. when you add more hidden layers and increase depth of neural network
244.

What are the steps for using a gradient descent algorithm?
1)Calculate error between the actual value and the predicted value
2)Reiterate until you find the best weights of network
3)Pass an input through the network and get values from output layer
4)Initialize random weight and bias
5)Go to each neurons which contributes to the error and change its respective values to reduce the error

A. 1, 2, 3, 4, 5
B. 4, 3, 1, 5, 2
C. 3, 2, 1, 5, 4
D. 5, 4, 3, 2, 1
Answer» B. 4, 3, 1, 5, 2
245.

A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 10 and 30 respectively. What will be the output?

A. 238
B. 76
C. 248
D. 348
Answer» D. 348
246.

Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional network.

A. true
B. false
Answer» B. false
247.

The F-test

A. an omnibus test
B. considers the reduction in error when moving from the complete model to the reduced model
C. considers the reduction in error when moving from the reduced model to the complete model
D. can only be conceptualized as a reduction in error
Answer» C. considers the reduction in error when moving from the reduced model to the complete model
248.

What is true about an ensembled classifier?
1. Classifiers that are more “sure” can vote with more conviction
2. Classifiers can be more “sure” about a particular part of the space
3. Most of the times, it performs better than a single classifier

A. 1 and 2
B. 1 and 3
C. 2 and 3
D. all of the above
Answer» D. all of the above
249.

Which of the following option is / are correct regarding benefits of ensemble model?
1. Better performance
2. Generalized models
3. Better interpretability

A. 1 and 3
B. 2 and 3
C. 1 and 2
D. 1, 2 and 3
Answer» C. 1 and 2
250.

Which of the following can be true for selecting base learners for an ensemble?
1. Different learners can come from same algorithm with different hyper parameters
2. Different learners can come from different algorithms
3. Different learners can come from different training spaces

A. 1
B. 2
C. 1 and 3
D. 1, 2 and 3
Answer» D. 1, 2 and 3
251.

True or False: Ensemble learning can only be applied to supervised learning methods.

A. true
B. false
Answer» B. false
252.

True or False: Ensembles will yield bad results when there is significant diversity among the models. Note: All individual models have meaningful and good predictions.

A. true
B. false
Answer» B. false
253.

Which of the following is / are true about weak learners used in ensemble model?
1. They have low variance and they don’t usually overfit
2. They have high bias, so they can not solve hard learning problems
3. They have high variance and they don’t usually overfit

A. 1 and 2
B. 1 and 3
C. 2 and 3
D. none of these
Answer» A. 1 and 2
254.

True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model.

A. true
B. false
Answer» A. true
255.

If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?

A. yes
B. no
C. can’t say
Answer» B. no
256.

Generally, an ensemble method works better, if the individual base models have ____________? Note: Suppose each individual base models have accuracy greater than 50%.

A. less correlation among predictions
B. high correlation among predictions
C. correlation does not have any impact on ensemble output
D. none of the above
Answer» A. less correlation among predictions
257.

In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure?

Hint: Persons are like base models of ensemble method.

A. bagging
B. boosting
C. a or b
D. none of these
Answer» A. bagging
258.

Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35.
Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction?
Note: All classifiers are independent of each other

A. 0.05
B. 0.06
C. 0.07
D. 0.09
Answer» B. 0.06
259.

In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea

A. true
B. false
Answer» A. true
260.

Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms?
1. Max number of samples
2. Max features
3. Bootstrapping of samples
4. Bootstrapping of features

A. 1 and 3
B. 2 and 3
C. 1 and 2
D. all of above
Answer» D. all of above
261.

How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?

A. model capacity increases in increase in dropout rate
B. model capacity decreases in increase in dropout rate
C. model capacity is not affected on increase in dropout rate
D. none of these
Answer» B. model capacity decreases in increase in dropout rate
262.

True or False: Dropout is computationally expensive technique w.r.t. bagging

A. true
B. false
Answer» B. false
263.

Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps?
Note: You have more than 1000 models predictions
1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set.
2. Start with empty ensemble
3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set

A. 1-2-3
B. 1-3-4
C. 2-1-3
D. none of above
Answer» D. none of above
264.

Suppose, you have 2000 different models with their predictions and want to ensemble predictions of best x models. Now, which of the following can be a possible method to select the best x models for an ensemble?

A. step wise forward selection
B. step wise backward elimination
C. both
D. none of above
Answer» C. both
265.

Below are the two ensemble models:
1. E1(M1, M2, M3) and
2. E2(M4, M5, M6)
Above, Mx is the individual base models.
Which of the following are more likely to choose if following conditions for E1 and E2 are given?
E1: Individual Models accuracies are high but models are of the same type or in another term less diverse
E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature

A. e1
B. e2
C. any of e1 and e2
D. none of these
Answer» B. e2
266.

True or False: In boosting, individual base learners can be parallel.

A. true
B. false
Answer» B. false
267.

Which of the following is true about bagging?
1. Bagging can be parallel
2. The aim of bagging is to reduce bias not variance
3. Bagging helps in reducing overfitting

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. all of these
Answer» C. 1 and 3
268.

Suppose you are using stacking with n different machine learning algorithms with k folds on data.

Which of the following is true about one level (m base models + 1 stacker) stacking?

Note:
Here, we are working on binary classification problem
All base models are trained on all features
You are using k folds for base models

A. you will have only k features after the first stage
B. you will have only m features after the first stage
C. you will have k+m features after the first stage
D. you will have k*n features after the first stage
Answer» B. you will have only m features after the first stage
269.

Which of the following is the difference between stacking and blending?

A. stacking has less stable cv compared to blending
B. in blending, you create out of fold prediction
C. stacking is simpler than blending
D. none of these
Answer» D. none of these
270.

Which of the following can be one of the steps in stacking?
1. Divide the training data into k folds
2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold
3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. all of above
Answer» A. 1 and 2
271.

Q25. Which of the following are advantages of stacking?
1) More robust model
2) better prediction
3) Lower time of execution

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. all of the above
Answer» A. 1 and 2
272.

Which of the following are correct statement(s) about stacking?
A machine learning model is trained on predictions of multiple machine learning models
A Logistic regression will definitely work better in the second stage as compared to other classification methods
First stage models are trained on full / partial feature space of training data

A. 1 and 2
B. 2 and 3
C. 1 and 3
D. all of above
Answer» C. 1 and 3
273.

Which of the following is true about weighted majority votes?
1. We want to give higher weights to better performing models
2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model
3. Voting is special case of weighted voting

A. 1 and 3
B. 2 and 3
C. 1 and 2
D. 1, 2 and 3
Answer» D. 1, 2 and 3
274.

Which of the following is true about averaging ensemble?

A. it can only be used in classification problem
B. it can only be used in regression problem
C. it can be used in both classification as well as regression
D. none of these
Answer» C. it can be used in both classification as well as regression
275.

How can we assign the weights to output of different models in an ensemble?
1. Use an algorithm to return the optimal weights
2. Choose the weights using cross validation
3. Give high weights to more accurate models

A. 1 and 2
B. 1 and 3
C. 2 and 3
D. all of above
Answer» D. all of above
276.

Suppose you are given ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models?
Note: We are working on a regression problem
1. Median
2. Product
3. Average
4. Weighted sum
5. Minimum and Maximum
6. Generalized mean rule

A. 1, 3 and 4
B. 1,3 and 6
C. 1,3, 4 and 6
D. all of above
Answer» D. all of above
277.

In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.

A. bagging
B. 1,3 and 6
C. a or b
D. none of these
Answer» A. bagging
278.

If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?

A. yes
B. no
C. can’t say
Answer» B. no
279.

Which of the following is NOT supervised learning?

A. pca
B. decision tree
C. linear regression
D. naive bayesian
Answer» A. pca
280.

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
281.

How can you avoid overfitting ?

A. by using a lot of data
B. by using inductive machine learning
C. by using validation only
D. none of above
Answer» A. by using a lot of data
282.

What are the popular algorithms of Machine Learning?

A. decision trees and neural networks (back propagation)
B. probabilistic networks and nearest neighbor
C. support vector machines
D. all
Answer» D. all
283.

What is Training set?

A. training set is used to test the accuracy of the hypotheses generated by the learner.
B. a set of data is used to discover the potentially predictive relationship.
C. both a & b
D. none of above
Answer» B. a set of data is used to discover the potentially predictive relationship.
284.

Common deep learning applications include        

A. image classification, real-time visual tracking
B. autonomous car driving, logistic optimization
C. bioinformatics, speech recognition
D. all above
Answer» D. all above
285.

what is the function of Supervised Learning?

A. classifications, predict time series, annotate strings
B. speech recognition, regression
C. both a & b
D. none of above
Answer» C. both a & b
286.

Commons unsupervised applications include

A. object segmentation
B. similarity detection
C. automatic labeling
D. all above
Answer» D. all above
287.

Reinforcement learning is particularly efficient when                         .

A. the environment is not completely deterministic
B. it\s often very dynamic
C. it\s impossible to have a precise error measure
D. all above
Answer» D. all above
288.

if there is only a discrete number of possible outcomes (called categories), the process becomes a           .

A. regression
B. classification.
C. modelfree
D. categories
Answer» B. classification.
289.

Which of the following are supervised learning applications

A. spam detection, pattern detection, natural language processing
B. image classification, real-time visual tracking
C. autonomous car driving, logistic optimization
D. bioinformatics, speech recognition
Answer» A. spam detection, pattern detection, natural language processing
290.

During the last few years, many              algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.

A. logical
B. classical
C. classification
D. none of above
Answer» D. none of above
291.

Which of the following sentence is correct?

A. machine learning relates with the study, design and
B. data mining can be defined as the process in which the
C. both a & b
D. none of the above
Answer» C. both a & b
292.

What is Overfitting in Machine learning?

A. when a statistical model describes random error or noise instead of underlying relationship overfitting occurs.
B. robots are programed so that they can perform the task based on data they gather from sensors.
C. while involving the process of learning overfitting occurs.
D. a set of data is used to discover the potentially predictive relationship
Answer» A. when a statistical model describes random error or noise instead of underlying relationship overfitting occurs.
293.

What is Test set?

A. test set is used to test the accuracy of the hypotheses generated by the learner.
B. it is a set of data is used to discover the potentially predictive relationship.
C. both a & b
D. none of above
Answer» A. test set is used to test the accuracy of the hypotheses generated by the learner.
294.

              is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value

A. removing the whole line
B. creating sub-model to predict those features
C. using an automatic strategy to input them according to the other known values
D. all above
Answer» B. creating sub-model to predict those features
295.

How it's possible to use a different placeholder through the parameter             .

A. regression
B. classification
C. random_state
D. missing_values
Answer» D. missing_values
296.

If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class              .

A. robustscaler
B. dictvectorizer
C. labelbinarizer
D. featurehasher
Answer» A. robustscaler
297.

scikit-learn also provides a class for per- sample normalization, Normalizer. It can apply              to each element of a dataset

A. max, l0 and l1 norms
B. max, l1 and l2 norms
C. max, l2 and l3 norms
D. max, l3 and l4 norms
Answer» B. max, l1 and l2 norms
298.

There are also many univariate methods that can be used in order to select the best features according to specific criteria based on              .

A. f-tests and p-values
B. chi-square
C. anova
D. all above
Answer» A. f-tests and p-values
299.

Which of the following selects only a subset of features belonging to a certain percentile

A. selectpercentile
B. featurehasher
C. selectkbest
D. all above
Answer» A. selectpercentile
300.

              performs a PCA with non-linearly separable data sets.

A. sparsepca
B. kernelpca
C. svd
D. none of the mentioned
Answer» B. kernelpca
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