Hands On Machine Learning with Python: Concepts and Applications for Beginners by John Anderson

Author:John Anderson [Anderson, John]
Language: eng
Format: epub
Publisher: AI Sciences LLC
Published: 2018-07-31T23:00:00+00:00

The plot does not indicate any 1 to 1 correlation between features, so all features are informative and provide discriminability.

We need to separate our columns into features and labels

features = dataset.drop(['Outcome'], axis=1)

labels = dataset['Outcome']

We would once again split our dataset into training set and test set as we want to train our model on the train split, then evaluate its performance on the test split.

from sklearn.model_selection import train_test_split

features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.25)

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