Q1
Q1 What is the primary goal of machine learning?
To manually program computers
To enable systems to learn from data
To simulate human intelligence
To enhance computer hardware speed
Q2
Q2 Which type of data is used in supervised learning?
Unlabeled data
Labeled data
Both
Neither
Q3
Q3 Which of the following is NOT a common machine learning task?
Classification
Regression
Sorting
Clustering
Q4
Q4 Which algorithm is commonly used for supervised learning?
K-Means Clustering
Linear Regression
Principal Component Analysis
DBSCAN
Q5
Q5 What is a key difference between supervised and unsupervised learning?
Supervised learning uses labeled data
Unsupervised learning requires labeled data
Supervised learning is faster
Unsupervised learning predicts labels
Q6
Q6 What does 'overfitting' refer to in machine learning?
The model performs well on new data
The model fits the training data too well
The model has insufficient data
The model uses too few features
Q7
Q7 In which scenario would you apply machine learning?
To create a simple rules-based system
To develop predictions from historical data
To manually program every output
To increase computer speed
Q8
Q8 Which function from the sklearn library is used to split a dataset?
train_test_split()
split_data()
dataset_split()
data_train_test()
Q9
Q9 A model performs well on training data but poorly on test data. Why?
Underfitting
Overfitting
Insufficient data
Testing data errors
Q10
Q10 Which of the following is an example of supervised learning?
K-Means Clustering
Linear Regression
Principal Component Analysis
DBSCAN
Q11
Q11 What type of learning is K-Means Clustering an example of?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Semi-supervised Learning
Q12
Q12 Which type of machine learning involves learning from labeled data?
Unsupervised Learning
Supervised Learning
Reinforcement Learning
None of the above
Q13
Q13 Which of the following is an example of unsupervised learning?
Decision Trees
Linear Regression
Principal Component Analysis
Logistic Regression
Q14
Q14 Which learning paradigm aims to maximize a reward signal through exploration and exploitation?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Semi-supervised Learning
Q15
Q15 What type of learning is used when a model learns from both labeled and unlabeled data?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Semi-supervised Learning
Q16
Q16 What distinguishes reinforcement learning from other learning paradigms?
The use of labeled data
Learning from unlabeled data
Learning through rewards and penalties
None of the above
Q17
Q17 Which function in Python can classify data using k-nearest neighbors (KNN)?
knn_classifier()
KNeighborsClassifier()
classify_neighbors()
knn_predict()
Q18
Q18 If a supervised learning model performs poorly on both training and testing sets, what is the likely issue?
Overfitting
Underfitting
Data errors
Model complexity
Q19
Q19 In supervised learning, what type of data is used to train the model?
Labeled data
Unlabeled data
Random data
Noise data
Q20
Q20 Which of the following is an example of a supervised learning algorithm?
K-Means
Linear Regression
PCA
DBSCAN
Q21
Q21 What is the key feature of supervised learning?
It uses only input data
It uses labeled data
It doesn't require data
It uses noise data
Q22
Q22 What is the main goal of supervised learning?
To find patterns in unlabeled data
To train a model using labeled data
To enhance computer hardware
To split data sets
Q23
Q23 Which of the following tasks is an example of classification in supervised learning?
Predicting house prices
Identifying email spam
Forecasting temperature
Clustering similar items
Q24
Q24 What is the difference between classification and regression in supervised learning?
Classification predicts continuous values
Regression predicts categorical labels
Classification predicts labels
Regression uses unlabeled data
Q25
Q25 Which function in Python is used for implementing linear regression in sklearn?
LinearRegression()
lin_reg()
linear_model()
regression_fit()
Q26
Q26 How can you calculate the accuracy of a classification model in sklearn?
accuracy_score()
calc_accuracy()
model_accuracy()
predict_accuracy()
Q27
Q27 Which method can be used to cross-validate a model in supervised learning using sklearn?
cross_val_score()
validate_model()
cross_validate()
k_fold_score()
Q28
Q28 A classification model performs well on training data but poorly on test data. What is the issue?
Overfitting
Underfitting
Data imbalance
Test data errors
Q29
Q29 A regression model shows very low variance but high bias. What is the likely problem?
Overfitting
Underfitting
Data imbalance
High dimensionality
Q30
Q30 When using cross-validation, the model performs poorly on all folds. What could be the cause?
Overfitting
Underfitting
Poor model selection
Insufficient data
