Q31
Q31 What is the primary goal of unsupervised learning?
To label data
To classify data
To find patterns in unlabeled data
To split data
Q32
Q32 Which of the following is an example of unsupervised learning?
Decision Trees
K-Means Clustering
Linear Regression
Logistic Regression
Q33
Q33 What type of data does unsupervised learning work with?
Labeled data
Unlabeled data
Both labeled and unlabeled data
Noise data
Q34
Q34 Which task is commonly solved by unsupervised learning?
Classification
Regression
Clustering
Time series forecasting
Q35
Q35 Which of the following methods is used for dimensionality reduction in unsupervised learning?
K-Means Clustering
Principal Component Analysis (PCA)
Logistic Regression
Support Vector Machines (SVM)
Q36
Q36 What differentiates hierarchical clustering from K-Means clustering?
K-Means is faster
Hierarchical clustering doesn't require the number of clusters to be specified
Hierarchical clustering is always more accurate
K-Means works better on large datasets
Q37
Q37 Which function in sklearn is used to implement K-Means clustering?
KMeansClustering()
KMeans()
ClusteringK()
cluster_KMeans()
Q38
Q38 Which Python library can be used to perform clustering with DBSCAN?
sklearn
numpy
pandas
matplotlib
Q39
Q39 How can you visualize the clusters generated by K-Means in Python using matplotlib?
plot_clusters()
plt.scatter()
cluster_plot()
plot_kmeans()
Q40
Q40 A K-Means clustering model creates uneven-sized clusters. What might be the issue?
Model overfitting
Data distribution
Cluster initialization
Lack of data
Q41
Q41 A DBSCAN clustering model fails to cluster some points. What could be the reason?
Points are too close
High density parameter
Noise in the data
Wrong clustering algorithm
Q42
Q42 K-Means clustering results change with different initializations. What could help resolve this?
Random state initialization
Lower number of clusters
Higher number of features
Scaling the data
Q43
Q43 What is the primary goal of regression in machine learning?
To classify data
To predict continuous values
To cluster data
To reduce dimensionality
Q44
Q44 Which of the following is an example of a linear regression problem?
Predicting the probability of a user clicking on an ad
Predicting house prices
Classifying emails
Detecting fraudulent transactions
Q45
Q45 What is the purpose of the cost function in linear regression?
To find clusters in data
To minimize the error between predicted and actual values
To predict categories
To maximize accuracy
Q46
Q46 What is the assumption of linearity in linear regression?
The relationship between variables is non-linear
The relationship between variables is linear
The variables are dependent on each other
The data is non-stationary
Q47
Q47 What is multicollinearity in the context of regression models?
When predictors are independent
When predictors are highly correlated
When predictors have missing values
When predictors are irrelevant
Q48
Q48 In polynomial regression, what is the degree of the polynomial?
The number of data points
The number of features
The highest power of the predictor variable
The number of clusters
Q49
Q49 Which function in sklearn is used for implementing linear regression?
lin_reg()
LinearRegression()
regression()
fit_regression()
Q50
Q50 How can you compute the Mean Squared Error (MSE) of a regression model in sklearn?
mean_squared_error()
calculate_mse()
regression_mse()
mse_calc()
Q51
Q51 Which library is used for implementing Ridge regression in Python?
numpy
pandas
sklearn
matplotlib
Q52
Q52 A linear regression model shows a high R-squared value but low predictive performance on test data. What could be the issue?
Overfitting
Underfitting
Data leakage
Insufficient data
Q53
Q53 A regression model shows a high variance but low bias. What might be the issue?
Overfitting
Underfitting
Multicollinearity
High-dimensionality
Q54
Q54 A Ridge regression model gives inconsistent results. What could be the cause?
Incorrect regularization parameter
Too many features
Underfitting
Wrong cost function
Q55
Q55 What is the goal of classification in machine learning?
To cluster data
To predict continuous values
To categorize data into predefined classes
To reduce dimensionality
Q56
Q56 Which of the following is an example of a binary classification problem?
Predicting house prices
Classifying emails as spam or not
Clustering customers
Predicting temperature
Q57
Q57 What is the purpose of a confusion matrix in classification problems?
To evaluate clustering results
To assess classification accuracy
To determine data distribution
To predict continuous values
Q58
Q58 Which metric is most suitable for imbalanced classification datasets?
Accuracy
Precision
Recall
F1-Score
Q59
Q59 What is the key difference between logistic regression and linear regression?
Logistic regression is used for classification
Linear regression is used for classification
Logistic regression predicts continuous values
Linear regression uses categorical labels
Q60
Q60 Which of the following algorithms is commonly used for multi-class classification?
K-Means
Naive Bayes
KNN
Logistic Regression
