(5+7)

(0.01*4)

(5+123)

(0+5)

2. [10 pts] Spark Decision Tree Classification and Evaluation - Input Data: daily_weather.esv with missing value removed - Binarize the 'max_wind_speed_9am' column such that days with 9-am max wind speed greater than (x6+7 ) are Windy days (Class 1 ), and otherwise Non-windy days (Class 0) - Predict the Windy/Non-windy days by using a decision tree classifier with the following parameters - 4 Input Features: ['air_pressure_9am', 'ar_temp_9am', 'relative_humidity_9am, 'rain_accumulation_9am'] - Train-test split: 0.8+(0.01x7) for training with a random seed of (x6+123) - Maximum depth of the tree: (x5+5) - For other parameters not specified above, feel free to set your own values - Question: What is the True Positive Rate of the test data? - CANVAS Submission: Upload the file below to Assignment Section "Final Section 2: Q2". - Python notebook (.ipynb) file with answer to the question in a Text cell of the .ipynb file