Questions

You are now able to build a machine learning model. Let's practice, putting our new skills to the test. In this chapter's GitHub repository, you will find a dataset that contains information about Android malware samples. Now you need to build your own model, following these instructions.

In the Chapter3-Practice GitHub repository, you will find a dataset that contains the feature vectors of more than 11,000 benign and malicious Android applications:

1. Load the dataset using the pandas python library, and this time, add the low_memory=False parameter. Search for what that parameter does.
2. Prepare the data that will be used for training.
3. Split the data with the test_size=0.33  parameter.
4. Create a set of classifiers that contains DecisionTreeClassifier(), RandomForestClassifier(n_estimators=100), and AdaBoostClassifier().
5. What is an AdaBoostClassifier()?
6. Train the model using the three classifiers and print out the metrics of every classifier.