A. To increase the size of the training dataset
B. To optimize the hyperparameters of a model
C. To evaluate the performance of a model on unseen data
D. To reduce the dimensionality of data

A. Classification
B. Clustering
C. Association Rule Mining
D. Regression

A. Sequential Pattern Mining
B. Temporal Clustering
C. Time Series Analysis
D. Chronological Regression

A. They reduce the complexity of models
B. They require less training data
C. They improve model accuracy by combining multiple models
D. They eliminate the need for feature selection

A. Confusion Matrix
B. Silhouette Score
C. ROC Curve
D. Precision-Recall Curve

A. To initialize the weights of the network
B. To calculate the output of the network
C. To adjust weights to minimize the error
D. To transform data into features

A. Support Vector Machines (SVM)
B. K-Means Clustering
C. Apriori Algorithm
D. Random Forest

A. Association Rule Mining
B. Regression Analysis
C. Outlier Detection
D. Dimensionality Reduction

A. Association Rule Mining
B. Regression Analysis
C. Outlier Detection
D. Dimensionality Reduction

A. To measure the purity of a node
B. To determine the depth of the tree
C. To calculate the distance between nodes
D. To evaluate the clustering tendency

A. Mean Absolute Error (MAE)
B. F1 Score
C. Clustering Coefficient
D. Lift

A. Decision Trees
B. Principal Component Analysis (PCA)
C. K-Nearest Neighbors (KNN)
D. Naive Bayes

A. To increase the data storage capacity
B. To improve data quality and make it suitable for analysis
C. To visualize data in real-time
D. To enhance data encryption

A. It requires large amounts of labeled data
B. It is not suitable for numerical data
C. It may converge to a local optimum
D. It cannot handle large datasets

A. The probability that two items are purchased together
B. The proportion of transactions that contain a specific item set
C. The likelihood of an item being a frequent itemset
D. The confidence level of a rule