Hi, today I am going to show you how to classify credit default using Classification Learner App.
First, we are going to import data into the MATLAB workspace.
It’s a sample credit card data.
In this dataset, we have customer id, credit limit, gender, educational level, age, number of delayed payments,
and historical billing amounts for period of 6 months.
Since we want to create a machine learning model to predict whether the client is going to default in the next 30 days or not,
our response variable is default where 1 is equal to default and zero otherwise.
Then, let’s go to the app tab and select the classification learner app.
As we click on the new session here, the app will automatically detect the variables in the MATLAB workspace.
Then, we can select the dataset that we want to use.
After that, the app will select if a variable is a predictor or a response based on the data type
However, we can also change its role or remove the variable.
Let’s deselect the ID as it is not relevant to default risk at all.
To protect against problems like overfitting, I am going to choose the hold out validation of 25%.
It means that we are going to use 75% of the data for training and the rest for validating the model performance.
Once we import the data into the app, we can visualize the relationship of predictor and response varoables using the scatter plot here
Next, we are going to apply machine learning algorithms to train the data.
To evaluate the default risk in practice, you may easily have hundreds of predictors.
In such case, you may want to apply principal component analysis or PCA too. PCA is a useful technique for dimensionality reduction.
However, we are going to leave this setting unchanged again as we don’t have too many predictors in this dataset.
And it’s time for applying classification algorithms. There are many classifiers available for you to choose in classification learners,
for examples, decision trees, nearest neighbors, and support vector machines.
Here you can select the classifier and click train one by one.
Or you can select a group of classifiers to train.
And when we want to apply multiple algorithms at the same time,
it is a good idea to use parallel computing to speed up the training process
In this example, logistic regression has the highest accuracy of 79.6%.
In addition to the accuracy, there are other diagnostic tools that can provide more details about the performance of each classifier.
To me, confusion matrix is one of the most intuitive and easiest metrics for evaluating the classification performance.
The diagonal elements in green represents the validation samples that are correctly classified.
On the other hand, the non-diagonal elements in red represents the validation samples that are misclassified.
You can use the options here to view other types of confusion matrix.
When you complete the classification process,
you can choose to directly export the model back to the MATLAB workspace or generate MATLAB code.
Let me choose this option.
Now, we can also see the details of classification workflow.
We can always customize the code and integrate it into other applications.
In this case, we may use this function to predict whether a client will default on the credit card in the next month or not.
If the model predicts that the client is likely to default, the bank may use such data to prevent additional credit risk.
For example, the bank may disapprove additional credit limit or even lower the existing credit limit.
Here, we can see the performance of the trained model in the testing dataset
To learn more about machine learning algorithms and its applications, you can visit this webpage here
There are examples and videos related to machine learning available here.
Thank you for watching this video.