advanced-algorithmic-trading

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lr_model.predict(X_test),
color=’black’,
linewidth=1.0
)
plt.xlabel("X")
plt.ylabel("y")
plt.show()
Figure 17.2: Plot of the test data x i vs y i values, overlaid with point-estimated line of best fit.
In subsequent chapters similar examples will be utilised for various machine learning techniques
such as Random Forests, Support Vector Machines and Boosted Trees. It will be shown
that the API for fitting such models is very similar to the above, making it extremely straightforward
to test new ML models in a rapid research-oriented manner.
17.5 Full Code
# lin_reg_distribution_plot.py
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from scipy.stats import norm