Question 3 Now that we have formatted our data, we can fit a model using sklearn's Ridge() class. We'll write a function that will take as input the features and response variables that we created in the last question, and returns a trained model. Function Specifications: Should take two numpy arrays as input in the form (X_train, y_train). Should return an sklearn Ridge model. The returned model should be fitted to the data. Hint: You may need to reshape the data within the function. You can use .reshape(-1, 1) to do this. [ ] ### START FUNCTION def train_model(X_train, y_train): # your code here return ### END FUNCTION [ ] data = get_year_pop('Aruba') (X_train, y_train), _ = feature_response_split(data) train_model(X_train, y_train).predict([[2017]]) array([[104468.15547163]]) Expected Outputs: train_model(X_train, y_train).predict([[2017]]) == array([[104468.15547163]])
Question 3 Now that we have formatted our data, we can fit a model using sklearn's Ridge() class. We'll write a function that will take as input the features and response variables that we created in the last question, and returns a trained model. Function Specifications: Should take two numpy arrays as input in the form (X_train, y_train). Should return an sklearn Ridge model. The returned model should be fitted to the data. Hint: You may need to reshape the data within the function. You can use .reshape(-1, 1) to do this. [ ] ### START FUNCTION def train_model(X_train, y_train): # your code here return ### END FUNCTION [ ] data = get_year_pop('Aruba') (X_train, y_train), _ = feature_response_split(data) train_model(X_train, y_train).predict([[2017]]) array([[104468.15547163]]) Expected Outputs: train_model(X_train, y_train).predict([[2017]]) == array([[104468.15547163]])
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question 3
Now that we have formatted our data, we can fit a model using sklearn's Ridge() class. We'll write a function that will take as input the features and response variables that we created in the last question, and returns a trained model.
Function Specifications:
- Should take two numpy arrays as input in the form (X_train, y_train).
- Should return an sklearn Ridge model.
- The returned model should be fitted to the data.
Hint: You may need to reshape the data within the function. You can use .reshape(-1, 1) to do this.
[ ]
### START FUNCTION
def train_model(X_train, y_train):
# your code here
return
### END FUNCTION
def train_model(X_train, y_train):
# your code here
return
### END FUNCTION
[ ]
data = get_year_pop('Aruba')
(X_train, y_train), _ = feature_response_split(data)
train_model(X_train, y_train).predict([[2017]])
array([[104468.15547163]])
Expected Outputs:
train_model(X_train, y_train).predict([[2017]]) == array([[104468.15547163]])Expert Solution
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