The built model is saved in 'mod_regress2

[jakaria7443]

Let's continue by building another model so we can compare the accuracy of both models and decide which model is better.
Create a new linear regression model for the 'train' set, but this time drop the 'x' and 'y' columns from the independent variables. That is, the 'price' of a diamond is all columns except 'x' and 'y'.
The prediction results are stored in 'result_regress2'.
예측 (mod_regress2, test)-> result_regress2
The actual and predicted values ​​are combined and stored in 'Final_Data2'.
cbind (Actual = test $ price, Predicted = result_regress2)-> Final_Data2 as.data.frame (Final_Data2)-> Final_Data2
We will also add prediction errors to 'Final_Data2'.
Take a look at “Final_Data2”:
Creating an Array of Java Objects
Find the root mean square error to mexico phone number material get the aggregate error: rmse2<-sqrt(mean(Final_Data2$error^2))
We can see that the second model is slightly better than the first model, as 'rmse2' is slightly smaller than 'rmse1'.
Passing by reference in Java
classification:
This is a recursive partitioning classification algorithm working with the 'car_purchase' dataset.
Let's split the data into 'train' and 'test' sets using the 'sample.split()' function from the 'caTools' package.
65% of the observations in the 'Purchased' column are labeled 'TRUE' and the rest are labeled 'FALSE'.
sample.split (car_purchase $ Purchased, SplitRatio = 0.65)-> split_values All observations with the 'TRUE' label are stored in the 'train' data and observations with the 'FALSE' label are assigned to the 'test' data.