Consider the ToyotaCorolla.csv dataset we studied in class. We fit a linear regression model for the target variable Price using predictors Age and Fuel_Type. Remember that Age is a numerical variable which captures age of a car in months as of August 2004, and Fuel_Type is a categorical variable with three possible values (Petrol, Diesel, CNG).

Consider the ToyotaCorolla.csv dataset we studied in class. We fit a linear regression model for the target variable Price using predictors Age and Fuel_Type. Remember that Age is a numerical variable which captures age of a car in months as of August 2004, and Fuel_Type is a categorical variable with three possible values (Petrol, Diesel, CNG).

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

See similar textbooks

Similar questions

You decide to run a simpler model to predict churn, using only the variables tenure (in months) and TotalCharges (in US$). The output is given below. The AIC of this model is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which model would be expected to give superior predictive performance? Actual ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.471e-01 5.360e-02 4.611 4.01e-06 *** ## tenure < 2e-16 *** -1.124e-01 5.816e-03 -19.334 ## TotalCharges 8.236e-04 5.618e-05 14.660 < 2e-16 *** ## No --- ## Signif. codes: 0 ## Yes Yes ## Null deviance: 5701.5 on 4921 ## Residual deviance: 4721.6 on 4919 ## AIC: 4727.6 515 345 ## (Dispersion parameter for binomial family taken to be 1) ## Predicted ***** No 795 3267 0.001 Confusion Matrix (Training) **** Actual 0.01 Yes No degrees of freedom degrees of freedom Yes The simpler model (with just tenure and TotalCharges) The full model (with all variables) 0.05 0.1 220 145 Predicted No 339…
You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy = (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives + false positives) Recall = (true positives)/ (true positives + false negatives) F1 score = (2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10
We've built a logistic regression model in RapidMiner, and would like to use it to make predictions for some new data points. Which operator do we need: Performance Apply Model Cross Validation O Nominal to Numerical.
You are given the true labels and the predicted probabilities from logistic regression model for N test examples. Approximately compute the AUC scores for ROC and PR curves.
Assume the following simple regression model, Y = β0 + β1X + ϵ ϵ ∼ N(0, σ^2 ) Now run the following R-code to generate values of σ^2 = sig2, β1 = beta1 and β0 = beta0. Simulate the parameters using the following codes: Code: # Simulation ## set.seed("12345") beta0 <- rnorm(1, mean = 0, sd = 1) ## The true beta0 beta1 <- runif(n = 1, min = 1, max = 3) ## The true beta1 sig2 <- rchisq(n = 1, df = 25) ## The true value of the error variance sigmaˆ2 ## Multiple simulation will require loops ## nsample <- 10 ## Sample size n.sim <- 100 ## The number of simulations sigX <- 0.2 ## The variances of X # # Simulate the predictor variable ## X <- rnorm(nsample, mean = 0, sd = sqrt(sigX)) Q1 Fix the sample size nsample = 10 . Here, the values of X are fixed. You just need to generate ϵ and Y . Execute 100 simulations (i.e., n.sim = 100). For each simulation, estimate the regression coefficients (β0, β1) and the error variance (σ 2 ). Calculate the mean of…
We estimate the following LRM for the relationship between COURSE_EVAL and AGE of the instructor: E(COURSE_EVAL | AGE) = 4.774 -0.2154 * log(AGE) where the AGE variable is transformed with the logarithmic function. Provide an interpretation of the coefficient estimate -0.2154.
In bivariate regression, the regression coefficient will be equal to rXY when:
The following dataset is a historic record of 14 houses that were sold in a small town in BC. The dataset is used to predict whether a new house in the same town will be sold in 10 days if listed with a specific price based on certain attributes. We are considering only four attributes (price, number of bedrooms, size, and distance to bus stop) just to simplify the calculations in this assignment but more attributes should be considered in real applications. House Price Number of Bedrooms Size (sqft) Distance to Bus-Stop House sold in 10 days? House 1 $300,000 1 3,500 sqft far No House 2 $300,000 1 3,500 sqft near No House 3 $250,000 1 3,500 sqft far Yes House 4 $350,000 2 3,500 sqft far Yes House 5 $350,000 3 5,000 sqft far Yes House 6 $350,000 3 5,000 sqft near No House 7 $250,000 3 5,000 sqft near Yes House 8 $300,000 2 3,500 sqft far No House 9…
We want to create an extra feature for the following data with only one feature (x) to use in a multiple linear regression model. What feature should we add? 6.0 V 5.5 50 40 35 30 -1.00 Linear Cubic Quadratic Logarithmic -0.50 -0.25 0:00 0.25 0.50 0.75 1.00