Consider the code presented below: from sklearn.linear_model import LinearRegression Im=LinearRegression() X = df[['highway-mpg']] Y = df['price'] Im.fit(X, Y) Yhat-Im.predict(X) What is the variable that contains the estimated values by the model? a. O b. O C. O d. Y Yhat Im X

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Q: pleas dont use AI.

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Q: b) Which of the following h(x) is used for regression? O O h(x) = [v Wixi i=0 h(x) = sign(wix;)) i=0

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A:

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A: Solution:

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Time Complexity

In computer science, the computational complexity that is measured in terms of time is referred to as time complexity that defines how long it takes a computer to execute an algorithm. An algorithm's time complexity is measured by the the amount of time required to execute each statement of code in the algorithm. If a statement is set to execute repetitively, the number of times it will be N multiplied by the time it takes to run that function each time is equal .