CSP315/ITP314 Final Quiz #2

In linear regression, the ranges for all attributes in the scoring data must be within the ranges for the corresponding attributes in the training data when using linear regression.

• True

• False

Like discriminant analysis, linear regression is a predictive model, and thus will need an attribute to be designated as the_____________ or the thing that we want to predict.

• label

• role

• model

• data set

This data mining model is used to determine how close a given observation to an imaginary line representing the average, or center of all point in the data set.

• k means clustering

• discriminant analysis

• linear regression

• correlation

The formula for calculating a prediction using linear regression is y=mx + b. The y in the formula is the _______________.

• target

• independent variable

• coefficient

• intercept

The formula for calculating a prediction using linear regression is y = mx + b. The m in the formula is the _______________

• target

• independent variable

• coefficient

• intercept

The formula for calculating a prediction using linear regression is y = mx + b. The b in the formula is the _______________

• target

• independent variable

• coefficient

• intercept

The formula for calculating a prediction using linear regression is y = mx + b. The x in the formula is the _______________

• target

• independent variable

• coefficient

• intercept

It is important to remember that linear regression uses numeric data types for all its attributes.

• True

• False

What is the primary purpose of discriminant analysis in machine learning?

• To minimize bias

• To maximize variance

• To classify data into different classes

• To reduce overfitting

Which Python library is commonly used to implement discriminant analysis?

• scikit-learn

• NumPy

• Pandas

• TensorFlow