Q61
Q61 Which function in sklearn is used to implement logistic regression?
log_regression()
LogisticRegression()
reg_log()
log_reg()
Q62
Q62 How do you calculate the precision score in a classification task using sklearn?
precision_score()
precision()
calc_precision()
precision_calc()
Q63
Q63 Which library is used for implementing decision trees in Python?
numpy
pandas
sklearn
matplotlib
Q64
Q64 A classification model has high accuracy but low recall. What does this indicate?
Model is overfitting
Model has high false negatives
Model has high false positives
Data is imbalanced
Q65
Q65 A logistic regression model is overfitting on training data. What can be done to mitigate this?
Reduce the training data size
Increase the number of features
Apply regularization
Use higher learning rate
Q66
Q66 A decision tree model has a very high depth and performs poorly on test data. What could be the reason?
Overfitting
Underfitting
Insufficient training data
Wrong cost function
Q67
Q67 What is a decision tree in machine learning?
A model for regression tasks
A model for clustering
A tree-like structure for decision making
A method for dimensionality reduction
Q68
Q68 What is the purpose of pruning in decision trees?
To increase the depth of the tree
To reduce the size of the tree
To improve training speed
To prevent overfitting
Q69
Q69 How does a decision tree split data at each node?
By maximizing accuracy
By minimizing distance between data points
By maximizing information gain
By using random splits
Q70
Q70 What is the main advantage of using Random Forest over a single Decision Tree?
It reduces the complexity of the model
It reduces overfitting
It increases the depth of trees
It uses fewer features
Q71
Q71 What is the role of entropy in decision trees?
It measures the homogeneity of data
It measures model accuracy
It measures the distance between data points
It calculates the tree depth
Q72
Q72 Which function in sklearn is used to implement decision trees?
DecisionTree()
TreeDecision()
DecisionTreeClassifier()
ClassifierTree()
Q73
Q73 How can you visualize a decision tree in Python using sklearn?
visualize_tree()
plot_tree()
draw_tree()
show_tree()
Q74
Q74 Which sklearn function is used to implement a Random Forest Classifier?
RandomForest()
ForestClassifier()
RandomForestClassifier()
RandomClass()
Q75
Q75 How does the max_depth parameter in a decision tree model affect its performance?
Controls the number of features
Controls the depth of the tree
Controls the number of samples
Controls the number of trees
Q76
Q76 A decision tree is overfitting the training data. What can you do to resolve this?
Increase the tree depth
Use fewer features
Prune the tree
Increase the training data
Q77
Q77 A Random Forest model gives inconsistent predictions across different datasets. What could be the cause?
Not enough trees
Too many features
Overfitting in individual trees
No randomness in the dataset
Q78
Q78 A decision tree has poor performance on test data but excellent performance on training data. What is the issue?
High variance
Underfitting
Overfitting
Data imbalance
Q79
Q79 What is the primary goal of clustering algorithms?
To label data
To classify data
To group similar data points
To reduce dimensionality
Q80
Q80 Which of the following is an example of a density-based clustering algorithm?
K-Means
DBSCAN
Hierarchical clustering
Agglomerative clustering
Q81
Q81 What is the key difference between K-Means and Hierarchical clustering?
K-Means requires predefined number of clusters
Hierarchical clustering is faster
K-Means doesn't need distance metrics
Hierarchical clustering cannot be used for large datasets
Q82
Q82 Which clustering algorithm does not require specifying the number of clusters in advance?
K-Means
DBSCAN
K-Nearest Neighbors
PCA
Q83
Q83 Which function in sklearn is used to implement K-Means clustering?
kmeans_clustering()
KMeans()
cluster_means()
cluster_KMeans()
Q84
Q84 How do you specify the number of clusters in K-Means clustering using sklearn?
num_clusters()
cluster_count()
n_clusters
num_clust
Q85
Q85 Which function is used to implement the DBSCAN algorithm in sklearn?
dbscan_clustering()
DBSCAN()
density_cluster()
cluster_density
Q86
Q86 In K-Means, what is the effect of using a large number of clusters (n_clusters)?
Increases overfitting
Increases the number of iterations
Increases randomness
Improves accuracy
Q87
Q87 A K-Means model produces clusters of very different sizes. What could be the reason?
Wrong distance metric
Data is not scaled
Overfitting
High dimensionality
Q88
Q88 A DBSCAN model labels a large number of points as noise. What could be the cause?
High epsilon value
Low min_samples value
Low epsilon value
High min_samples value
Q89
Q89 A hierarchical clustering algorithm produces a large number of small clusters. What could resolve this issue?
Increase the number of clusters
Reduce the number of clusters
Increase the linkage criterion
Use a different distance metric
Q90
Q90 What is the purpose of dimensionality reduction in machine learning?
To increase model complexity
To remove redundant features
To reduce model accuracy
To create more features
