Dataset is divided into:

• Training data

• Evaluation data

• Testing data

• Answer

What is the primary purpose of a confusion matrix in classification?

• To visualize the distribution of featu

• To evaluate the performance of a regression model

• To evaluate the performance of a classification model

• To determine the correlation between features

In a confusion matrix, which cell represents the number of correctly predicted positive instances?

• True Positives (TP)

• False Positives (FP)

• True Negatives (TN)

• False Negatives (FN)

How is precision calculated from a confusion matrix?

• TP / (TP + FN)

• TP / (TP + FP)

• (TP + TN) / (TP + FP + TN + FN)

• TN / (TN + FP)

Which of the following best describes the process of selecting splits in a decision tree?

• Randomly choosing features to split

• Using statistical tests to determine the best split

• Splitting nodes based on criteria such as Gini impurity or information gain

• Selecting splits based on the smallest number of instances

What is the purpose of pruning in decision trees?

• To add more branches to the tree

• To remove branches that have little importance and reduce overfitting

• To increase the accuracy of the tree on training data

• To increase the depth of the tree

If a model has a high number of false positives, what does this indicate?

• • The model is predicting many true negatives correctly.

• The model is incorrectly predicting negative instances as positive.

• The model has a high precision.

• The model has a high recall.

How can the rules generated by a decision tree be best described?

• As a set of algebraic equations

• As a series of if-then-else statements

• As a linear regression equation

• As a confusion matrix

How does class imbalance affect the confusion matrix and evaluation metrics?

• It can lead to misleading accuracy, favoring the majority class

• It does not affect the confusion matrix or metrics

• It ensures better precision and recall

• It simplifies the model building process

How does the ROC curve complement the confusion matrix in evaluating a classification model?

• It visualizes the trade-off between true positive rate and false positive rate

• It shows the correlation between features

• It directly displays precision and recall values

• It helps in tuning hyperparameters