You are building a model to predict the number of points

Question 1

You are building a model to predict the number of points (“margin”) by which Team A will beat Team B in a basketball game. Your input features are (1) whether or not it it is a home game for Team A (2) average number of points Team A scored in its past 7 games and (3) average number of points Team B scored in its past 7 games. Which of these is a linear model suitable for machine learning?

1 point

margin = (avg_points_A – avg_points_B)

margin = b + w1 * is_home_game + w2 * avg_points_A + w3 * avg_points_B

margin = w1 * is_home + w1^2 * avg_points_A + w1^3 * avg_points_B

margin = w1 * is_home + w2 * (avg_points_A – avg_points_B)^3

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A list of categorical features

A list of bucketized features

A list of numeric features

A list of categorical or bucketized features

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Reuse weights learned in one problem in another problem

Identify similar sets of inputs for clustering

Create a lower-dimensional representation of the input space

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Question 2

Feature crosses are more common in modern machine learning because:

1 point

Feature crosses work only with neural networks

People didn’t know about feature crosses 10 years ago

Feature crosses require GPUs in order to compute efficiently

Feature crosses memorize, and that is okay only if you have extremely large datasets.

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You are building a model to predict the number of points (“margin”) by which Team A will beat Team B in a basketball game

Feature crosses >>> You are building a model to predict the number of points (“margin”) by which Team A will beat Team B in a basketball game >>> Feature Engineering

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