Exploring Multiclass Classification

What is the main idea behind Support Vector Machines?

• Support Vector Machines focus on clustering data points into groups.

• Support Vector Machines are used to reduce the dimensionality of data.

• The main idea behind Support Vector Machines is to find the optimal hyperplane that maximizes the margin between different classes.

• The main idea is to create a decision tree for classification.

How do Decision Trees make decisions based on data?

• Decision Trees rely on random sampling of data points for decisions.

• Decision Trees make decisions by averaging all feature values.

• Decision Trees classify data by using a single feature only.

• Decision Trees make decisions by recursively splitting data based on feature values until reaching a final classification.

What is the role of 'k' in K-Nearest Neighbors?

• 'k' determines the number of nearest neighbors used for classification or regression.

• 'k' indicates the maximum number of features to consider.

• 'k' is the threshold for data normalization.

• 'k' represents the distance metric used in the algorithm.

What assumption does Naive Bayes make about the features?

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

• Features are independent of the class label.

• Features are dependent on the class label.

• Features are conditionally independent given the class label.

In multilabel classification, how does it differ from multiclass classification?

• Multilabel classification allows multiple labels per instance, while multiclass classification allows only one label per instance.

• Multilabel classification requires a single label per instance.

• Multilabel classification is used for regression tasks.

• Multiclass classification allows multiple labels per instance.

What is the kernel trick in Support Vector Machines?

• The kernel trick reduces the dimensionality of the data.

• The kernel trick is a method for linear regression.

• The kernel trick allows SVM to operate in higher-dimensional spaces using kernel functions.

• The kernel trick is used to visualize data in 2D space.

What are the advantages of using Decision Trees?

• Requires extensive data preprocessing

• Advantages of using Decision Trees include interpretability, ability to handle various data types, minimal preprocessing, capturing non-linear relationships, robustness to outliers, and versatility in classification and regression.

• Limited to binary classification

• High computational cost

How does K-Nearest Neighbors handle ties when classifying?

• KNN resolves ties by selecting the class with the highest frequency or using a random choice among tied classes.

• KNN uses the average distance to resolve ties.

• KNN ignores ties and randomly selects a class.

• KNN always chooses the first class in the list.

What types of data can Naive Bayes be applied to?

• Naive Bayes can be applied to categorical, text, and continuous data.

• Naive Bayes is suitable for image and audio data.

• Naive Bayes can only be applied to numerical data.

• Naive Bayes is limited to binary data only.

What is the significance of the Gini index in Decision Trees?

• The Gini index is used to calculate the overall performance of the model.

• The Gini index determines the number of leaves in a Decision Tree.

• The Gini index is significant in Decision Trees as it helps determine the best feature to split the data by measuring impurity.

• The Gini index measures the accuracy of predictions in Decision Trees.