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 FUNCTIONdef 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]])

Python function to fit a model using sklearn's Ridge() class and return the trained model is, from…

Answered: Question 3 Now that we have…

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

[ ]

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]])

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