Neural network – multilayer perceptron

Using a neural network in scikit-learn is straightforward and proceeds as follows:

1. Load the data.
2. Scale the data with a standard scaler.
3. Do a hyperparameter search. Begin by varying the alpha parameter.

Getting ready

Load the medium-sized California housing dataset that we used in Chapter 9, Tree Algorithms and Ensembles:

%matplotlib inline

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.datasets import fetch_california_housing

cali_housing = fetch_california_housing()

X = cali_housing.data
y = cali_housing.target

Bin the target variable so that the target train set and target test set are a bit more similar. Then use a stratified train/test split:

bins = np.arange(6)
binned_y = np.digitize(y, bins)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,stratify=binned_y)

How to do it...

1. Begin by scaling the input variables. Train the scaler only on...