Machine Learning Quiz

What is precision in the context of machine learning metrics?

• The ratio of true positives to the total number of predictions.

• The ratio of true positives to the sum of true positives and false negatives.

• The ratio of true positives to the sum of true positives and false positives.

• The ratio of true negatives to the total number of predictions.

Which of the following is an advantage of ensemble methods?

• They reduce the complexity of the models.

• They improve the robustness and accuracy of predictions.

• They require less data for training.

• They eliminate the need for cross-validation.

In the voting ensemble method, the final prediction is made based on:

• The majority vote of the base models.

• The average of the predictions of the base models.

• The weighted sum of the predictions of the base models.

• All of the above.

Bagging helps in improving model performance by:

• Reducing bias.

• Reducing variance.

• Increasing bias.

• Increasing variance.

Which of the following is a boosting algorithm?

• Random Forest

• Gradient Boosting

• K-Nearest Neighbors

• Support Vector Machine

In stacking, the model that combines the predictions of base models is called:

• Primary model

• Secondary model

• Meta model

• Base model

A decision tree splits data at each node based on:

• Random selection

• The feature that gives the highest reduction in impurity

• The feature with the most missing values

• The feature with the least variance

Which measure is commonly used to evaluate the splits in a decision tree for classification tasks?

• Variance

• Mean Absolute Error

• Gini Impurity

• Euclidean Distance

In regression trees, the best split is chosen based on the reduction in:

• Gini Impurity

• Entropy

• Variance

• Accuracy

Random Forests improve the accuracy of predictions by:

• Using a single decision tree

• Combining multiple decision trees trained on random subsets of the data

• Using linear regression

• Ignoring certain features