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Regression Analysis with Python by Alberto Boschetti, Luca Massaron

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Online mini-batch learning

From the previous section, we've learned an interesting lesson: for big data, always use SGD-based learners because they are faster, and they do scale.

Now, in this section, let's consider this regression dataset:

  • Massive number of observations: 2M
  • Large number of features: 100
  • Noisy dataset

The X_train matrix is composed of 200 million elements, and may not completely fit in memory (on a machine with 4 GB RAM); the testing set is composed of 10,000 observations.

Let's first create the datasets, and print the memory footprint of the biggest one:

In: # Let's generate a 1M dataset X_train, X_test, y_train, y_test = generate_dataset(2000000, 10000, 100, 10.0) print("Size of X_train is [GB]:", X_train.size * X_train[0,0].itemsize/1E9) ...

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