10 Questions
What is the difference between classification and prediction in data analysis?
Classification predicts categorical labels, while prediction models continuous valued functions.
Provide an example of a classification model.
Categorizing bankloan applications as safe or risky.
What kind of model is used for predicting the expenditures of potential customers on computer equipment?
Prediction model
What is regression analysis commonly used for?
Numeric prediction
Describe the two steps involved in the data classification process.
Learning step and classification step
What is the purpose of the learning step in classification?
To build a classifier by analyzing a training set of database tuples and their associated class labels.
Explain the significance of the class label attribute in the learning step.
The class label attribute determines the predefined class to which each tuple belongs. It is categorical, discrete-valued, and unordered.
Why is it important to use a test set for estimating the predictive accuracy of the classifier?
Using a test set helps to provide a more realistic estimate of the classifier's accuracy, as it is independent of the training tuples and can detect overfitting.
What is the purpose of data cleaning in the context of classification or prediction?
Data cleaning aims to remove or reduce noise, treat missing values, and improve the accuracy, efficiency, and scalability of the classification or prediction process.
How are training tuples referred to in the context of classification?
Training tuples can be referred to as samples, examples, instances, data points, or objects.
Explore the concepts of classification and prediction in data analysis, and how they are used to extract models and predict future trends. Learn about building models to categorize data into classes or predict continuous valued functions.
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