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Иннов ИС non_longes

Programming · non_longest_answer_test.txt

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Иннов ИС non_longes

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#1

Which statement correctly describes the difference between classification and regression?

  1. Classification predicts continuous values, while regression predicts categories
  2. Classification predicts categories, while regression predicts continuous values
  3. Both classification and regression always predict only binary labels
  4. Regression is used only for unsupervised learning
#2

Which of the following is an example of a regression task?

  1. Detecting whether an email is spam or not
  2. Classifying a tumor as benign or malignant
  3. Predicting the price of a house
  4. Grouping customers into clusters
#3

What does generalization mean in machine learning?

  1. Memorizing all training examples perfectly
  2. Performing well on new, unseen data
  3. Using only one feature for prediction
  4. Removing the test set from the workflow
#4

What usually happens when the number of neighbors in KNN increases?

  1. The model becomes more complex and unstable
  2. The model becomes smoother and simpler
  3. The model stops using distance
  4. The model becomes a clustering algorithm
#5

Which is an advantage of KNN?

  1. It never requires data preprocessing
  2. It is simple and easy to interpret
  3. It always works best with thousands of features
  4. It does not require a value of K
#6

For one input feature, the prediction of a linear regression model is represented as:

  1. A circle
  2. A straight line
  3. A decision tree
  4. A cluster center only
#7

What is the main purpose of Ridge Regression?

  1. To increase the complexity of a model without control
  2. To reduce overfitting by shrinking coefficients
  3. To create cluster labels
  4. To remove the intercept from the model
#8

Logistic Regression is mainly used for which type of task?

  1. Classification
  2. Clustering
  3. Dimensionality reduction
  4. Image reconstruction only
#9

LinearSVC is an example of which type of model?

  1. Unsupervised clustering model
  2. Linear classification model
  3. Dimensionality reduction model
  4. Feature scaling model
#10

In Logistic Regression and LinearSVC, what does parameter C control?

  1. The number of clusters
  2. The strength of regularization
  3. The number of principal components
  4. The size of the dataset
#11

What does a small value of C usually mean in linear classification models?

  1. Stronger regularization and a simpler model
  2. Weaker regularization and a more complex model
  3. More clusters
  4. No training process
#12

GaussianNB is most suitable for which type of data?

  1. Only binary data
  2. Continuous numerical data
  3. Only image pixels with no labels
  4. Only cluster labels
#13

BernoulliNB is commonly suitable for which type of data?

  1. Binary features
  2. Continuous regression targets
  3. Hierarchical clusters
  4. Principal components only
#14

DecisionTreeClassifier is used for:

  1. Classification tasks
  2. Only clustering tasks
  3. Only PCA visualization
  4. Only feature scaling
#15

DecisionTreeRegressor is used for:

  1. Class label prediction only
  2. Predicting continuous target values
  3. Scaling features to 0 and 1
  4. Calculating Naive Bayes probabilities
#16

What is the main idea of Random Forest?

  1. To use one linear model
  2. To combine many decision trees
  3. To reduce data to two dimensions
  4. To calculate only Euclidean distance
#17

What does the n_estimators parameter usually define in Random Forest?

  1. The number of features
  2. The number of trees
  3. The number of classes
  4. The number of clusters
#18

In NMF, coefficients are required to be:

  1. Negative
  2. Non-negative
  3. Only binary labels
  4. Random strings
#19

What does K represent in K-Means?

  1. Number of features
  2. Number of clusters
  3. Number of test samples
  4. Number of principal components
#20

Which is a limitation of K-Means?

  1. It never requires choosing the number of clusters
  2. It can struggle with complex cluster shapes
  3. It cannot use numerical features
  4. It is always supervised
#21

What is a dendrogram used for?

  1. Visualizing the hierarchy of clusters
  2. Showing logistic regression coefficients
  3. Scaling numerical data
  4. Testing Random Forest parallelism
#22

What can DBSCAN identify in addition to clusters?

  1. Regression coefficients
  2. Noise or outlier points
  3. Principal components
  4. Feature importance from trees
#23

In DBSCAN, what does epsilon define?

  1. Number of trees
  2. Neighborhood radius around a point
  3. Number of classes
  4. Learning rate of logistic regression
#24

What is edge computing?

  1. Processing data only in a distant central server
  2. Processing data closer to where it is generated
  3. Deleting local devices
  4. Using no network connection at all