Separating Data for Training and Testing in an ML Pipeline
Question
You are developing a ML pipeline using Python SDK, and you have to separate your data for training the model as well as for testing the trained model.
You got a code snippet from your less experienced teammate, which is a great help, if it works.
You have to check if it does the job.
By its description, the script loads data from the default datastore and separates the 70% of the observations for training and the rest of them for testing, by using the scikit-learn package, in a reproducible way.
from sklearn.model_selection import train_test_split # Get the experiment run context run = Run.get_context() # load data print("Loading Data...") diabetes_data = run.input_datasets['diabetes_train'].to_pandas_dataframe() # Separate features and labels X, y = diabetes_data[['Pregnancies','PlasmaGlucose', 'DiastolicBloodPressure','BMI','Age']].values, diabetes['Diabetic'].values # Split data into training set and test set X_test, X_train, y_test, y_train = train_test_split(X, y, test_size=0.30, random_state=None) After reviewing the code, do you think it does its job as described?
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Explanations
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A. B.The code snippet loads data from the default datastore and separates it into features and labels. Then it uses the train_test_split() method from the scikit-learn package to split the data into training and test sets. However, there is a problem with the order of the variables in the train_test_split() function.
The correct order for the train_test_split() method is (X, y), not (y, X). The current implementation swaps the order and assigns the training set to X_test and the test set to X_train, which is incorrect. As a result, the model will be trained on the test set, and its performance on the test set will be artificially high.
Therefore, the correct implementation of the train_test_split() method should be:
scssX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=None) In addition, it's worth noting that the test_size parameter is set to 0.30, which means that 30% of the data will be used for testing and 70% for training. This split may not be optimal for all datasets, and it's important to evaluate the model's performance on a separate validation set before deploying it in production.
In conclusion, the code snippet does not do its job as described, and the correct implementation of the train_test_split() method is (X_train, X_test, y_train, y_test) = train_test_split(X, y, test_size=0.30, random_state=None).
