Machine Learning Quiz 2

What is a decision tree primarily used for?

• Classification and regression tasks in machine learning.

• Data visualization in business intelligence.

• Natural language processing tasks in AI.

• Data storage and retrieval in databases.

Which of the following is a characteristic of the Naive Bayes classifier?

• Uses a complex neural network architecture

• Requires a large amount of training data

• Independence of features given the class label

• Features are dependent on each other

What does the 'k' in k-nearest neighbors represent?

• The number of nearest neighbors to consider.

• The total number of data points in the dataset.

• The type of algorithm used for classification.

• The distance metric used in the algorithm.

What is the main advantage of using a random forest over a single decision tree?

• Less complexity in model structure than a single decision tree.

• Faster training time compared to a single decision tree.

• Improved accuracy and reduced overfitting through ensemble learning.

• Higher interpretability than a single decision tree.

What assumption does the Naive Bayes classifier make about features?

• Features are dependent on the class label.

• Features are conditionally independent given the class label.

• Features are correlated with each other regardless of the class label.

• Features are independent of the class label.

What is the primary goal of k-means clustering?

• To reduce the dimensionality of data.

• To classify data into predefined categories.

• To partition data into k distinct clusters based on similarity.

• To visualize data in a two-dimensional space.

What is the role of the 'k' parameter in k-nearest neighbors?

• The 'k' parameter is used to set the learning rate in KNN.

• The 'k' parameter specifies the number of nearest neighbors to consider in the KNN algorithm.

• The 'k' parameter defines the maximum number of features to consider in KNN.

• The 'k' parameter determines the distance metric used in KNN.

How does random forest reduce the risk of overfitting?

• Random forest reduces overfitting by averaging multiple decision trees trained on different data subsets.

• Random forest uses a single decision tree to make predictions.

• Random forest increases overfitting by using the same data for all trees.

• Random forest eliminates the need for data preprocessing before training.

What type of data can k-means clustering be applied to?

• Numerical data

• Image data

• Categorical data

• Text data

Which of the following is NOT a component of a decision tree?

• Data point

• Leaf node

• Branching factor

• Root node