Interpreting coefficients

Remember that origin airport, org, has eight possible values (ORD, SFO, JFK, LGA, SMF, SJC, TUS and OGG) which have been one-hot encoded to seven dummy variables in org_dummy.

The values for km and org_dummy have been assembled into features, which has eight columns with sparse representation. Column indices in features are as follows:

0 — km
1 — ORD
2 — SFO
3 — JFK
4 — LGA
5 — SMF
6 — SJC and
7 — TUS.

Note that OGG does not appear in this list because it is the reference level for the origin airport category.

An instance of LinearRegression is available in regression. In this exercise you'll be using the intercept and coefficients attributes to interpret the model.

The coefficients attribute is a list, where the first element indicates how flight duration changes with flight distance.

This exercise is part of the course

Machine Learning with PySpark

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Exercise instructions

Find the average speed in km per hour. This will be different to the value that you got earlier because your model is now more sophisticated.
What's the average time on the ground at OGG?
What's the average time on the ground at JFK?
What's the average time on the ground at LGA?

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Average speed in km per hour
avg_speed_hour = ____
print(avg_speed_hour)

# Average minutes on ground at OGG
inter = regression.____
print(inter)

# Average minutes on ground at JFK
avg_ground_jfk = ____ + regression.____[____]
print(avg_ground_jfk)

# Average minutes on ground at LGA
avg_ground_lga = ____ + regression.____[____]
print(avg_ground_lga)

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