Applied Deep Learning with Python

Using k-fold cross-validation and validation curves in Python with scikit-learn

1. If you've not already done so, start the NotebookApp and open the chapter-2- workbook.ipynb file. Scroll down to Subtopic K-fold cross-validation and validation curves.

The training data should already be in the notebook's memory, but let's reload it as a reminder of what exactly we're working with.

2. Load the data and select the satisfaction_level and last_evaluation features for the training/validation set. We will not use the train-test split this time because we are going to use k-fold validation instead. Run the cell containing the following code:

      df = pd.read_csv('../data/hr-analytics/hr_data_processed.csv') features = ['satisfaction_level', 'last_evaluation'] ...