ITP314/CSP315 Final Quiz #1

What is the purpose of a training dataset in analytics?

• To evaluate the model's performance

• To test the model's generalization on new data

• To train the model's parameters using historical data

In analytics, what is the purpose of a scoring dataset?

• To evaluate the model's training performance

• To apply the trained model to new, unseen data

• To fine-tune the model's hyperparameters

Which of the following statements is true about a scoring dataset?

• It is used for training a model

• It contains labeled data for model evaluation

• It is used to make predictions using a trained model

Why is it important to preprocess data before training a model?

• To make the dataset larger

• To improve the model's interpretability

• To handle missing values, scale features, and encode categorical variables

What is the risk of overfitting in the context of training a model?

• The model performs well on the training data but poorly on new data

• The model is too simple to capture underlying patterns

• The model has high bias

In K-Means, what does "K" represent?

• Number of features

• Number of clusters

• Number of iterations

• Number of observations

What type of data does K-Means clustering work best with?

• Ordinal data

• Categorical data

• Numerical data

• Text data

How does K-Means initialize cluster centroids?

• Using the mean of the dataset

• Using random points in the dataset

• Using the largest data points

• Using fixed points outside the dataset

What happens when K is chosen to be too high in K-Means?

• Clusters become too general.

• Clusters become too specific and overfitted.

• K-Means automatically corrects the number of clusters.

• It leads to infinite iterations.

What is the method commonly used to determine the optimal number of clusters (K)?

• Gradient descent

• Silhouette analysis

• Elbow method

• Cross-validation