How can compare decision tree and linear regression using CrossValidated or XValidated?
Attached are some relavent pictures of my set up and stats on the target variable:
1) The Setup of my .rmp , 2) Picture of the Histogram of the Target Variable, 3) Plot of CO2 (target variable) vs. Primary Principle Component
I best compare models with Cross Validation to figure out especially between categorical models like decision trees vs. numerical models like linear regression. I have been learning about cross validation in my Rapidminer class, but I am not 100% sure what exactly accuracy, precision, and recall are for classification prediction and regression prediction operators. For example, I would like to use precision and class recall to compare models, but I don't know what they might be for regression because the confusion matrix is based on nomial label not a numerical label.
So How can compare decision tree and linear regression using CrossValidated or XValidated? What statistic or metric could I use?
Below are the stats output from my results:
Target Variable Stats
CO2 Emissions Average: 87,405.93 Deviation: 628 363.80
Performance of Linear Regression:
root_mean_squared_error: 34,017.261 +/ 5548.473 (mikro: 34467.846 +/ 0.000)
normalized_absolute_error: 0.151 +/ 0.040 (mikro: 0.140)
Performance of Decision Tree:
accuracy: 90.65% +/ 4.13% (mikro: 90.64%)
root_mean_squared_error: 0.282 +/ 0.063 (mikro: 0.289 +/ 0.000)
normalized_absolute_error: 3.143 +/ 5.800 (mikro: 1.209)
Avg. Class Precision: 62.1%
Avg. Class Recall: 68%
Performance of Decision Random Forest:
accuracy: 82.14% +/ 1.92% (mikro: 82.14%)
root_mean_squared_error: 0.392 +/ 0.025 (mikro: 0.393 +/ 0.000)
normalized_absolute_error: 1.017 +/ 0.117 (mikro: 0.993)
Avg. Class Precision: 86.3%
Avg. Class Recall: 40%
Performance of Neural Network:
root_mean_squared_error: 23,815.976 +/ 4305.543 (mikro: 24211.353 +/ 0.00)
normalized_absolute_error: 0.126 +/ 0.037 (mikro: 0.122)
Performance of General Linearized Model (Default values):
root_mean_squared_error: 21,9027.497 +/ 45537.878
normalized_absolute_error: 1.017 +/ 0.117 (mikro: 0.993)
Best Answer

JEdward RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 578 Unicorn
What you might want to do is transform your Performance for the regression into a classification result by Discretizing the Label & Prediction variables with the same rules you applied for you Domain Expert defined bins.
Then you are comparing like for like.
However
One caution I would give on your classification prediction is to think about how your classification model is measured against misclassifications.
Imagine you have a numerical label with values 1 to 10.
After binning your label has the following nominal values.
Value 1: 13
Value 2: 46
Value 3: 79
Value 4: 10
Now if your classification model predicts something with an original numeric value of 3 and it predicts that it is in group 'Value 2: 46', then although this is a misclassification it is actually more accurate than if it had predicted 'Value 4: 10'. However, just looking purely at Accuracy, Precision & Recall won't reflect this. Both misclassifications as 'Value 4:10' and 'Value 2:46' have the same performance value 0... which is just not correct.
I would recommend that you use the Performance (Costs) operator and create a misclassification costs matrix. That way you can reflect that misclassifications in nearby groups are 'less costly' than those in more distance groups.
1
Answers
Thanks, I eventually realized that it really is like trying to compare fruit and vegitables.