Classification Methods Quiz

What is Decision Tree Induction?

• Decision Tree Induction is a cooking technique

• Decision Tree Induction is a type of dance

• Decision Tree Induction is a programming language

• Decision Tree Induction is a machine learning algorithm used for classification and regression tasks.

Explain the concept of Bayes Classification Methods.

• Bayes Classification Methods do not consider prior probabilities when making predictions.

• Bayes Classification Methods are probabilistic algorithms based on Bayes' Theorem that assign the class with the highest probability to be the predicted class.

• Bayes Classification Methods always assign equal probabilities to all classes.

• Bayes Classification Methods are deterministic algorithms based on Bayes' Theorem.

How does Rule-Based Classification work?

• Rule-Based Classification is based on unsupervised learning techniques.

• Rule-Based Classification relies on neural networks to classify data points.

• Rule-Based Classification uses if-then statements to classify data points based on predefined rules.

• Rule-Based Classification uses random guessing to classify data points.

What are Ensemble Learning Techniques in classification?

• Ensemble Learning Techniques do not aim to improve predictive performance

• Ensemble Learning Techniques focus on individual model accuracy rather than combined performance

• Ensemble Learning Techniques involve using only one model for classification

• Ensemble Learning Techniques in classification involve combining multiple models to improve the overall predictive performance.

Why is Feature Selection important in Classification?

• Feature selection has no impact on classification performance.

• Feature selection helps in reducing overfitting, improving accuracy, and decreasing computational cost.

• Feature selection only works for specific types of data and is not generally applicable.

• Feature selection increases overfitting, reduces accuracy, and increases computational cost.

List some common Evaluation Metrics used in Classification.

• Accuracy, Precision, Recall, F1 Score, ROC-AUC

• Sensitivity

• Specificity

• Mean Absolute Error

What are the advantages of Decision Tree Induction?

• High computational complexity

• Sensitive to noisy data

• Limited to binary outcomes

• Interpretability, handling both numerical and categorical data, requiring little data preprocessing, and being able to handle non-linear relationships.

Discuss the limitations of Bayes Classification Methods.

• The limitations of Bayes Classification Methods include the assumption of independence between features, the need for a large amount of training data to estimate probabilities accurately, and the inability to handle missing data effectively.

• Bayes Classification Methods are not suitable for text classification

• Bayes Classification Methods require manual feature selection

• Bayes Classification Methods assume perfect data distribution

How can Ensemble Learning Techniques improve classification accuracy?

• Ensemble Learning Techniques combine multiple models to improve classification accuracy.

• Ensemble Learning Techniques only work well with a single model

• Ensemble Learning Techniques have no impact on classification accuracy

• Ensemble Learning Techniques decrease the number of models used for classification

Explain the process of Feature Selection in Classification.

• The process of feature selection in classification includes methods like Filter methods, Wrapper methods, and Embedded methods. Filter methods evaluate the relevance of features based on statistical measures. Wrapper methods use a specific machine learning algorithm to evaluate the importance of features. Embedded methods incorporate feature selection within the model building process itself.

• Feature selection is only applicable in regression, not classification

• Feature selection is not necessary in classification

• Feature selection involves only visual inspection of data