3. Supervised Learning – Key Steps

Activity 3.01: Data Partitioning on a Handwritten Digit Dataset

Solution:

1. Import all the required elements to split a dataset, as well as the load_digits function from scikit-learn to load the digits dataset. Use the following code to do so:

   from sklearn.datasets import load_digits
   import pandas as pd
   from sklearn.model_selection import train_test_split
   from sklearn.model_selection import KFold

2. Load the digits dataset and create Pandas DataFrames containing the features and target matrices:

   digits = load_digits()
   X = pd.DataFrame(digits.data)
   Y = pd.DataFrame(digits.target)
   print(X.shape, Y.shape)

   The shape of your features and target matrices should be as follows, respectively:

   (1797, 64) (1797, 1)

3. Perform the conventional split approach, using a split ratio of 60/20/20%.

   Using the train_test_split function, split the data into an initial train set and a test set:

   X_new, X_test, \
   Y_new, Y_test = train_test_split(X, Y, test_size...