Branch-AI/one_exercise_per_file/week02/day03/ex02/readme.md

Exercise 2 Scaler

The goal of this exercise is to learn to scale a data set. There are various scaling techniques, we will focus on StandardScaler from scikit learn.

We will use a tiny data set for this exercise that we will generate by ourselves:

X_train = np.array([[ 1., -1.,  2.],
                     [ 2.,  0.,  0.],
                     [ 0.,  1., -1.]])

1. Fit the StandardScaler on the data and scale X_train using fit_transform. Compute the mean and std on axis 0.

2. Scale the test set using the StandardScaler fitted on the train set.

X_test = np.array([[ 2., -1.,  1.],
                     [ 3.,  3.,  -1.],
                     [ 1.,  1., 1.]])

WARNING: If the data is split in train and test set, it is extremely important to apply the same scaling the test data. As the model is trained on scaled data, if it takes as input unscaled data, it returns incorrect values.

Resources:

• https://medium.com/technofunnel/what-when-why-feature-scaling-for-machine-learning-standard-minmax-scaler-49e64c510422

• https://scikit-learn.org/stable/modules/preprocessing.html