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.

*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.

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.

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

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

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.

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

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