Binary Classification Model

Model Scenario

With the Binary Classification model, you can analyze the importance of the features in a binary column (2 distinct values). You can also do a prediction of what is most likely to happen according to different parameters. 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 scenario, you must have a Target Feature which has to be a binary column, which means it could be a column with only:

  • 1 and 0

  • Yes and No

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.

Model Results

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 6 tabs: Feature Importance, Feature Impact, Model Fit, Model Performance, Training Result, and Details Tabs.

Feature Importance

To see which feature has the 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.

Feature Impact

The Feature Impact Tab represents a chart where you can see some features, using counting or percentage to see how they impact the target. So you can select the feature that you want to analyze and how it is shown.

Model Fit

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.

Model Performance

The Model Performance Tab shows the performance of your model by creating the confusion matrix that reveals classification errors.

It also gives precisions about the confusion matrix and the predictions.

And finally, some information on the model metrics are following.

Training Results

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.

Now you have everything to understand the Binary Classification Model.


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.

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