How can compare decision tree and linear regression using Cross-Validated or X-Validated?

mylane3mylane3 Member Posts: 3 Contributor I
edited November 2018 in Help

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 Cross-Validated or X-Validated?  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

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    JEdwardJEdward RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 578 Unicorn
    Solution Accepted

    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. 


    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: 1-3

    Value 2: 4-6

    Value 3: 7-9

    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: 4-6', 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 2:4-6' 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. 




  • Options
    mylane3mylane3 Member Posts: 3 Contributor I

    Thanks, I eventually realized that it really is like trying to compare fruit and vegitables.

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