What is the purpose of data normalization in data preprocessing?
A) To reduce data redundancy
B) To scale the data to a specific range
C) To remove outliers from the dataset
D) To replace missing values with appropriate estimates
Answer: B
Which of the following is a supervised learning algorithm?
A) K-means clustering
B) Decision tree
C) Principal Component Analysis (PCA)
D) Apriori algorithm
Answer: B
What does ROC curve stand for in the context of classification models?
A) Receiver Operating Characteristic
B) Relative Operating Characteristic
C) Random Order Classifier
D) Regression Output Classifier
Answer: A
Which statistical measure describes the spread of data in a dataset?
A) Mean
B) Median
C) Standard deviation
D) Mode
Answer: C
In machine learning, what does the term “overfitting” refer to?
A) The model is too simple to capture the underlying structure of the data.
B) The model performs well on unseen data.
C) The model memorizes the training data and performs poorly on unseen data.
D) The model has high bias and low variance.
Answer: C
Which of the following is used to evaluate the performance of a regression model?
A) Confusion matrix
B) F1 score
C) Mean Absolute Error (MAE)
D) Recall
Answer: C
What is the purpose of the elbow method in K-means clustering?
A) To determine the optimal number of clusters
B) To calculate the centroids of the clusters
C) To assign data points to the nearest cluster
D) To measure the similarity between clusters
Answer: A
Which of the following techniques is used for feature selection in machine learning?
A) Support Vector Machines (SVM)
B) Principal Component Analysis (PCA)
C) K-nearest neighbors (KNN)
D) Recursive Feature Elimination (RFE)
Answer: D
What is the main advantage of using ensemble learning methods?
A) They are computationally less expensive.
B) They can combine multiple models to improve performance.
C) They are robust to overfitting.
D) They work well with high-dimensional data.
Answer: B
Which of the following is not a classification algorithm?
A) Logistic Regression
B) K-means clustering
C) Decision Tree
D) Naive Bayes
Answer: B
What does the term “precision” represent in a classification problem?
A) The ratio of true positives to the total actual positive cases
B) The ratio of true positives to the total predicted positive cases
C) The ratio of true positives to the total true positives and false negatives
D) The ratio of true positives to the total true positives and false positives
Answer: D
What does AUC stand for in the context of evaluating classification models?
A) Area Under Curve
B) Area Under Classifier
C) Area Under Confidence
D) Area Under Comparison
Answer: A
Which of the following is used to handle imbalanced datasets in classification problems?
A) Random oversampling
B) Random undersampling
C) SMOTE (Synthetic Minority Over-sampling Technique)
D) All of the above
Answer: D
Which algorithm is commonly used for text classification tasks?
A) K-means clustering
B) Decision tree
C) Naive Bayes
D) Principal Component Analysis (PCA)
Answer: C
What does the term “bias” refer to in the context of machine learning models?
A) The inability of the model to capture the true relationship between features and target variable
B) The ability of the model to generalize to unseen data
C) The difference between predicted values and actual values
D) The amount by which the model’s predictions deviate from the true values
Answer: A
Which of the following is a dimensionality reduction technique?
A) Random Forest
B) Gradient Boosting
C) Singular Value Decomposition (SVD)
D) AdaBoost
Answer: C
What is the purpose of cross-validation in machine learning?
A) To estimate how well a model will generalize to new data
B) To increase the size of the training dataset
C) To reduce overfitting in the model
D) To decrease the computational time required for training
Answer: A
Which evaluation metric is sensitive to class imbalance?
A) Accuracy
B) Precision
C) Recall
D) F1 score
Answer: B
Which of the following algorithms is used for clustering?
A) Linear Regression
B) K-means
C) Support Vector Machines (SVM)
D) Random Forest
Answer: B
What is the purpose of regularization in machine learning?
A) To increase the complexity of the model
B) To reduce the complexity of the model
C) To memorize the training data
D) To overfit the training data
Answer: B
Which of the following is a technique used for time series forecasting?
A) K-means clustering
B) Random Forest
C) ARIMA (AutoRegressive Integrated Moving Average)
D) Principal Component Analysis (PCA)
Answer: C
What does the term “recall” represent in a classification problem?
A) The ratio of true positives to the total actual positive cases
B) The ratio of true positives to the total predicted positive cases
C) The ratio of true positives to the total true positives and false negatives
D) The ratio of true positives to the total true positives and false positives
Answer: C
Which of the following techniques is used for data imputation?
A) K-means clustering
B) Principal Component Analysis (PCA)
C) Mean imputation
D) One-hot encoding
Answer: C
Which of the following is a non-parametric machine learning algorithm?
A) Linear Regression
B) Decision Tree
C) K-nearest neighbors (KNN)
D) Logistic Regression
Answer: C
What is the purpose of feature scaling in machine learning?
A) To transform categorical variables into numerical variables
B) To normalize the distribution of features
C) To increase the dimensionality of the dataset
D) To decrease the number of features in the dataset
Answer: B
Which algorithm is used for anomaly detection?
A) K-means clustering
B) Decision tree
C) Isolation Forest
D) Random Forest
Answer: C
Which of the following is a hyperparameter for a Support Vector Machine (SVM)?
A) Number of neighbors
B) Learning rate
C) Kernel type
D) Number of clusters
Answer: C
Which of the following algorithms is used for association rule mining?
A) K-means clustering
B) Apriori algorithm
C) Random Forest
D) Gradient Boosting
Answer: B
What does the term “bagging” refer to in ensemble learning?
A) Training multiple models sequentially
B) Training multiple models in parallel
C) Training multiple models on different subsets of the data
D) Training multiple models using the same dataset
Answer: C
Which of the following techniques is used for feature extraction in natural language processing?
A) Support Vector Machines (SVM)
B) Word Embeddings
C) K-means clustering
D) AdaBoost
Answer: B
What does the term “PCA” stand for in dimensionality reduction?
A) Principal Component Analysis
B) Principal Classification Algorithm
C) Predictive Component Analysis
D) Principal Correlation Analysis
Answer: A
Which of the following is a tree-based ensemble learning algorithm?
A) Linear Regression
B) Random Forest
C) Logistic Regression
D) K-means clustering
Answer: B
What is the purpose of a confusion matrix in classification problems?
A) To visualize the performance of a classification model
B) To compute the accuracy of a classification model
C) To calculate the mean squared error of a classification model
D) To estimate the number of true positives in a classification model
Answer: A
Which of the following is a similarity measure used in K-nearest neighbors (KNN)?
A) Manhattan distance
B) Cosine similarity
C) Euclidean distance
D) All of the above
Answer: D
What is the purpose of grid search in machine learning?
A) To find the optimal hyperparameters for a model
B) To visualize the decision boundaries of a model
C) To identify the outliers in a dataset
D) To perform feature selection
Answer: A
Which of the following is used for feature engineering in natural language processing?
A) Term Frequency-Inverse Document Frequency (TF-IDF)
B) Principal Component Analysis (PCA)
C) Recursive Feature Elimination (RFE)
D) Lasso Regression
Answer: A
Which of the following algorithms is sensitive to feature scaling?
A) Decision Tree
B) K-nearest neighbors (KNN)
C) Naive Bayes
D) Random Forest
Answer: B
What does the term “bag-of-words” represent in natural language processing?
A) A model that predicts the probability of a sequence of words
B) A model that represents text as a collection of words without considering the order
C) A model that captures the semantic meaning of words
D) A model that converts text into numerical vectors
Answer: B
Which of the following techniques is used for feature encoding in machine learning?
A) Principal Component Analysis (PCA)
B) One-hot encoding
C) Ridge Regression
D) Support Vector Machines (SVM)
Answer: B
What is the purpose of early stopping in neural networks?
A) To prevent the model from converging too quickly
B) To prevent overfitting by stopping training when the validation error increases
C) To increase the learning rate during training
D) To decrease the batch size during training
Answer: B
Which of the following is used for time series decomposition?
A) ARIMA
B) Autoencoder
C) Long Short-Term Memory (LSTM)
D) Exponential Smoothing
Answer: D
What does the term “batch size” represent in neural networks?
A) The number of epochs in training
B) The number of samples processed before the model is updated
C) The number of layers in the neural network
D) The number of neurons in each layer of the neural network
Answer: B
Which of the following algorithms is an example of unsupervised learning?
A) Linear Regression
B) K-means clustering
C) Decision tree
D) Support Vector Machines (SVM)
Answer: B
What is the purpose of dropout in neural networks?
A) To reduce the learning rate during training
B) To increase the number of neurons in each layer
C) To prevent overfitting by randomly deactivating neurons during training
D) To increase the batch size during training
Answer: C
Which of the following is a performance measure for regression models?
A) Confusion matrix
B) ROC curve
C) Mean Squared Error (MSE)
D) F1 score
Answer: C
What does the term “R-squared” represent in regression analysis?
A) The proportion of the variance in the dependent variable that is predictable from the independent variables
B) The slope of the regression line
C) The intercept of the regression line
D) The mean absolute error of the model
Answer: A
Which of the following algorithms is suitable for handling nonlinear relationships in data?
A) Linear Regression
B) Decision Tree
C) Logistic Regression
D) Ridge Regression
Answer: B
What does the term “word embedding” refer to in natural language processing?
A) A technique for representing words as dense vectors in a continuous vector space
B) A technique for identifying stopwords in text data
C) A technique for stemming words in text data
D) A technique for tokenizing text data
Answer: A
Which of the following is a measure of feature importance in tree-based models?
A) Mean Absolute Error (MAE)
B) Coefficient of determination (R-squared)
C) Gini impurity
D) Confusion matrix
Answer: C
What is the purpose of the Adam optimizer in neural networks?
A) To initialize the weights of the neural network
B) To adjust the learning rate during training
C) To minimize the loss function by updating the network weights
D) To prevent overfitting during training
Answer: C
