Quiz Part VI: Machine Learning Basics
Chapter 15: Unsupervised Learning
- Which algorithm is commonly used for clustering?
A) Linear Regression
B) K-means Clustering
C) Logistic Regression
D) Decision Trees
- Which technique is used for dimensionality reduction?
A) Logistic Regression
B) K-means Clustering
C) Principal Component Analysis (PCA)
D) Random Forests
- What is the primary goal of anomaly detection?
A) To find the average of a dataset
B) To group similar data points together
C) To find outliers in a dataset
D) To reduce the dimensions of a dataset
Chapter 15: Unsupervised Learning
- Which algorithm is commonly used for clustering?
A) Linear Regression
B) K-means Clustering
C) Logistic Regression
D) Decision Trees
- Which technique is used for dimensionality reduction?
A) Logistic Regression
B) K-means Clustering
C) Principal Component Analysis (PCA)
D) Random Forests
- What is the primary goal of anomaly detection?
A) To find the average of a dataset
B) To group similar data points together
C) To find outliers in a dataset
D) To reduce the dimensions of a dataset
Chapter 15: Unsupervised Learning
- Which algorithm is commonly used for clustering?
A) Linear Regression
B) K-means Clustering
C) Logistic Regression
D) Decision Trees
- Which technique is used for dimensionality reduction?
A) Logistic Regression
B) K-means Clustering
C) Principal Component Analysis (PCA)
D) Random Forests
- What is the primary goal of anomaly detection?
A) To find the average of a dataset
B) To group similar data points together
C) To find outliers in a dataset
D) To reduce the dimensions of a dataset
Chapter 15: Unsupervised Learning
- Which algorithm is commonly used for clustering?
A) Linear Regression
B) K-means Clustering
C) Logistic Regression
D) Decision Trees
- Which technique is used for dimensionality reduction?
A) Logistic Regression
B) K-means Clustering
C) Principal Component Analysis (PCA)
D) Random Forests
- What is the primary goal of anomaly detection?
A) To find the average of a dataset
B) To group similar data points together
C) To find outliers in a dataset
D) To reduce the dimensions of a dataset