Q61
Q61 A pie chart in Matplotlib displays incorrect proportions. What could be the issue?
Wrong data labels
Missing data
Incorrect sum of values
Invalid chart type
Q62
Q62 A scatter plot shows overlapping points, making it hard to interpret. What can improve its readability?
Increase marker size
Add jitter
Use smaller axes
Change chart type
Q63
Q63 A line chart is difficult to interpret due to too many data points. What is the best approach to simplify it?
Aggregate data
Remove the chart
Use larger axes
Switch to bar chart
Q64
Q64 What is the primary objective of machine learning?
To clean data
To make predictions based on data
To create databases
To improve hardware
Q65
Q65 Which of the following is a supervised learning algorithm?
K-Means
Decision Trees
DBSCAN
Principal Component Analysis
Q66
Q66 What is overfitting in machine learning?
Model performs poorly on training data
Model performs well on training data but poorly on new data
Model is too simple
Model has no bias
Q67
Q67 What is the purpose of a loss function in machine learning?
To evaluate model predictions
To split datasets
To improve visualization
To standardize data
Q68
Q68 Why is it important to split data into training and testing datasets?
To increase dataset size
To evaluate model performance on unseen data
To clean data
To preprocess features
Q69
Q69 Which Python library provides the train_test_split function?
NumPy
Pandas
scikit-learn
Matplotlib
Q70
Q70 How do you train a linear regression model using scikit-learn?
model.fit(X, y)
model.train(X, y)
model.learn(X, y)
model.predict(X, y)
Q71
Q71 Which scikit-learn function is used to calculate the accuracy of a classification model?
classification_report
accuracy_score
score
confusion_matrix
Q72
Q72 A model's predictions have high bias. What could be the likely issue?
Overfitting
Underfitting
Feature scaling
Incorrect testing data
Q73
Q73 A classification model achieves 99% accuracy on the training set but only 60% on the test set. What is the issue?
Overfitting
Underfitting
Data imbalance
Feature scaling
Q74
Q74 After training a regression model, the residuals show a clear pattern. What does this imply?
Model is accurate
Model assumptions are violated
Feature scaling is wrong
Data is balanced
Q75
Q75 What is the key difference between supervised and unsupervised learning?
Supervised uses labeled data, unsupervised does not
Both use labeled data
Both use unlabeled data
Unsupervised requires labels
Q76
Q76 Which of the following is an example of a supervised learning algorithm?
K-Means
Linear Regression
Hierarchical Clustering
PCA
Q77
Q77 Which task is best suited for unsupervised learning?
Predicting house prices
Identifying customer segments
Spam classification
Stock price prediction
Q78
Q78 What metric is commonly used to evaluate a regression model in supervised learning?
Accuracy
Mean Squared Error (MSE)
Precision
Silhouette score
Q79
Q79 Why is clustering considered an unsupervised learning technique?
It requires labeled data
It uses supervised models
It finds patterns in unlabeled data
It predicts outcomes
Q80
Q80 Which Python library provides the KMeans function for clustering?
NumPy
Pandas
scikit-learn
Matplotlib
Q81
Q81 How do you fit a decision tree classifier in scikit-learn?
model.train(X, y)
model.fit(X, y)
model.learn(X, y)
model.split(X, y)
Q82
Q82 Which function in scikit-learn is used to calculate the silhouette score for a clustering model?
silhouette_score()
cluster_score()
clustering_score()
silhouette_metric()
Q83
Q83 How do you specify the number of clusters in the KMeans algorithm using scikit-learn?
KMeans(n_clusters=n)
KMeans(clusters=n)
KMeans(n=n)
KMeans(n_cluster=n)
Q84
Q84 A supervised model performs poorly on unseen data. What is the likely issue?
Data leakage
Underfitting
Incorrect loss function
Missing labels
Q85
Q85 A clustering model produces inconsistent results. What could be the likely cause?
Wrong feature scaling
Labeled data
High accuracy
Balanced dataset
Q86
Q86 After applying KMeans, one cluster has very few data points. What should you consider next?
Increase cluster count
Decrease cluster count
Visualize clusters
Change the algorithm
Q87
Q87 What is the primary goal of feature engineering in machine learning?
Improve model interpretability
Reduce dataset size
Enhance model performance
Avoid overfitting
Q88
Q88 Which technique is commonly used to handle categorical data in feature engineering?
Normalization
One-hot encoding
PCA
Standardization
Q89
Q89 Why is feature scaling important in machine learning?
Reduces model size
Improves convergence during training
Handles missing values
Reduces overfitting
Q90
Q90 What is feature selection?
Adding new features
Choosing the best features
Removing outliers
Scaling data
