Spliting employee data

Overfitting the dataset is a common problem in analytics. This happens when a model is working well on the dataset it was developed upon, but fails to generalize outside of it.

A train/test split is implemented to ensure model generalization: you develop the model using the training sample and try it out on the test sample later on.

In this exercise, you will split both target and features into train and test sets with 75%/25% ratio, respectively.

Deze oefening maakt deel uit van de cursus

HR Analytics: Predicting Employee Churn in Python

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Oefeninstructies

Import train_test_split from the sklearn.model_selection module
Use train_test_split() to split your dataset into training and testing sets
Assign 25% of your observations to the testing set

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import the function for splitting dataset into train and test
from sklearn.model_selection import ____

# Use that function to create the splits both for target and for features
# Set the test sample to be 25% of your observations
target_train, target_test, features_train, features_test = ____(target,features,____=0.25,random_state=42)

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