Supervised models for continuous target

Description

The process presents, using the dataset Concrete Compressive Strength, that how can we fit supervised data mining models for datasets with continuous target. We can use for this task the Regression operator, the Decision Tree operator, and the Neural Network operator, as well. By Regression operator linear regression model can be fitted. Decision trees can be given by the Decision Tree operator. Finally, the result of a Neural Network operator is a neural network that minimizes a predefined error function on the validation dataset. In each case, continuous target variable with continuous level metadata must be selected.

Input

Concrete Compressive Strength [UCI MLR] [Concrete]

In the preprocessing step, the dataset is partitioned for training, validation and test datasets with percent 60/20/20, respectively.

Output

A comparison of the resulting models are significantly different from the tools applied for discrete or binary target. Among the statistical indicators, we can use different information criterion (e.g. AIC, SBC) or the mean square error and the square root of the it. In this case there aro no such graphical tools as lift curve and classification chart. Instead, we can use the graphs showing averages of forecasts.

Figure 24.12. Statistics of the fitted models on the test dataset

Statistics of the fitted models on the test dataset

The figure below shows that the neural network and the linear regression behave fairly similarly.

Figure 24.13. Comparison of the fitted models by means of predictions

Comparison of the fitted models by means of predictions

Figure 24.14. The observed and predicted means plot

The observed and predicted means plot

The following curves is good if we are closer to the diagonal straight line.

Figure 24.15. The model scores

The model scores

In addition to the comparisons, one by one, we can examine the individual models. Below you can see the constructed decision tree, which is created by using F-statistics.

Figure 24.16. The decision tree for continuous target

The decision tree for continuous target

For the neural network model the weights of neurons can be visualized.

Figure 24.17. The weights of neural network after traning

The weights of neural network after traning

Interpretation of the results

Both the statistics and graphics show that the neural network model fits the best.

Video

Workflow

sas_regr_exp3.xml

Keywords

supervised learning
continuous target
decision tree
linear regression
neural network

Operators

Data Source
Decision Tree
Model Comparison
Neural Network
Data Partition
Regression