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

The estimated value is only the normal result you have per experiment when you let it run…

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

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

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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For the Linear Congruential method, assume the following parameters:Xo = 23,947, a = 2,902, c = 15,853 and M = 491.Feeling free to state any assumptions you deem necessary, define the first 10 random numbers.
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import numpy as np import pandas as pd from catboost import CatBoostRegressor from lightgbm import LGBMRegressor from sklearn.linear_model import Lasso from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from xgboost import XGBRegressor df=pd.read_csv('data.csv') X = df.drop('shares', axis=1) y = df['shares'] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.40, random_state=13) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=13) Ans:- # code here Q- Now let's train our first model - XGBoost. A link to the documentation: https://xgboost.readthedocs.io/en/latest/ We will use Scikit-Learn Wrapper interface for XGBoost (and the same logic applies to the following LightGBM and CatBoost models). Here, we work on the regression task - hence we will use XGBRegressor. Read…
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