PAML Week 2

Why is it important to do data preprocessing?

• To enhance the visual aesthetics of the dataset accuracy and efficiency

• To reduce the computational complexity of the model

• To ensure the data is suitable for ML models which also increases the accuracy and efficiency.

• To introduce variability and complexity into the dataset

What is the main goal of Support Vector Regression (SVR)?

• Classifying data into categories

• Predicting continuous numeric values for non-linear relationships

• Visualizing complex relationships in data

• Encoding categorical data

In the context of splitting the dataset, what is the purpose of the validation set?

• Training the model

• Fine-tuning hyperparameters and assessing model performance

• Evaluating the final model

• Encoding categorical data

What is the key characteristic of Simple Linear Regression?

• It uses multiple independent variables

• It predicts a numerical dependent variable using a single independent variable

• It is suitable for multi-class classification

• It predicts categorical variables

What is K in K Nearest Neighbour (KNN)?

• The number of neighbours to include in the majority voting process

• The number of features in the dataset

• The number of classes in classification

• The distance metric used for prediction

In Naive Bayes, what does the assumption of features being independent mean?

• Features have no impact on the outcome

• Features are not correlated with each other

• Features have equal importance

• Features are irrelevant for prediction

What does Precision measure in the context of classification?

• The proportion of true positive predictions out of all positive predictions

• The proportion of true negative predictions out of all negative predictions

• The overall accuracy of the model

• The proportion of true positive predictions out of all actual positive instances

When is the F1 Score particularly useful?

• When precision is more important than recall

• When recall is more important than precision

• When both precision and recall are important, and there is a need for balance

• When there are only two classes in classification

What does a Confusion Matrix visualize in classification?

• The actual classes in the dataset

• The predicted classes by the model

• The performance of a classification model by comparing predicted and actual class labels

• The distribution of data points in the feature space

Which machine learning task involves assigning multiple labels to a single input?

• Binary Classification

• Multi-Class Classification

• Multi-label Classification

• Regression