Regression Model
Last updated
Last updated
With the Regression model, you can see which regression matches your dataset. To get the best possible results, we will go through the basics of the Model Scenario. In Model Scenario, you select parameters related to the dataset and model.
To run the model, you have to choose a Target Feature first. The target refers to the variable or outcome that the model aims to predict or estimate. In this case, it should be a numerical column.
The next thing to do is choose all the Model Features that you want to analyze. You can choose which feature the model will be analyzed. Some of them cannot fit for model and it shows the reason for each one.
Now you can finish the process and run the scenario.
And then you have all the information about the status with the best model used and the training time.
Let's see how to interpret the results after we have run our model.
First, you have all the performance, based on the best model and its accuracy.
And then, the results consist of 5 tabs: Feature Importance, Feature Impact, Model Fit, Training Result, and Details Tabs.
To see which feature has more impact on the target, we have the Feature Importance Tab. It shows how much each feature impacts the target and on the right more details on them.
The Feature Impact Tab represents a chart where you can see some features to see how it impacts the target. So you can select the feature that you want to analyze.
The Model Fit Tab contains a graph with actual and predicted values. You can see which one is correct and incorrect. With visualization, you can see how well or poorly your model is performing.
In the Training Results Tab, you have all the information about all the models to see which one is the best, trained on 80% of the dataset and tested on the 20% left, to have the accuracy.
In the end, a table with all the values related to the Model Fit Tab, with much more, can be found on the Details Tab.
After building and analyzing a predictive model using Graphite Note, the "Predict" function allows you to apply the model to new data. This enables you to forecast outcomes or target variables based on different feature combinations, providing actionable insights for decision-making.
