Ada boost Regressor: Effect of parameters

Ada Boost Regressor

Ada Boost Regressor technique is tested here to see their accuracy in terms of output.

Python program:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from matplotlib.colors import ListedColormap
>>> from sklearn import ensemble, datasets
>>> iris = datasets.load_iris()
>>> x = iris.data[:, :2]
>>> y = iris.target
>>> h = .02
>>> cmap_bold = ListedColormap(['firebrick', 'lawngreen', 'b'])
>>> cmap_light = ListedColormap(['pink', 'palegreen', 'lightcyan'])




//Plotting the analysis//


a) Effect of number of estimators (n_estimators):
>>> for n_estimators in [1, 2, 15, 75, 250, 1000]:
...     clf = ensemble.AdaBoostRegressor(n_estimators=n_estimators)
...     clf.fit(x, y)
...     x_min, x_max = x[:, 0].min() -1, x[:, 0].max() +1
...     y_min, y_max = x[:, 1].min() -1, x[:, 1].max() +1
...     xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
...     z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
...     z = z.reshape(xx.shape)
...     plt.figure()
...     plt.pcolormesh(xx, yy, z, cmap=cmap_light)
...     plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=24)
...     plt.xlim(xx.min(), xx.max())
...     plt.ylim(yy.min(), yy.max())
...     plt.title("AdaBoostRegressor (n_estimators='%s')" %(n_estimators))
...

b) Effect of random state(random_state):
>>> for random_state in [1, 2, 15, 75, 250, 1000]:
...     clf = ensemble.AdaBoostRegressor(random_state=random_state)
...     clf.fit(x, y)
...     x_min, x_max = x[:, 0].min() -1, x[:, 0].max() +1
...     y_min, y_max = x[:, 1].min() -1, x[:, 1].max() +1
...     xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
...     z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
...     z = z.reshape(xx.shape)
...     plt.figure()
...     plt.pcolormesh(xx, yy, z, cmap=cmap_light)
...     plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=24)
...     plt.xlim(xx.min(), xx.max())
...     plt.ylim(yy.min(), yy.max())
...     plt.title("AdaBoostRegressor (random_state='%s')" %(random_state))
...

c) Effect of learning rate(learning_rate):
>>> for learning_rate in [1.0, 2.0, 15.0, 75.0, 250.0, 1000.0]:
...     clf = ensemble.AdaBoostRegressor(learning_rate=learning_rate)
...     clf.fit(x, y)
...     x_min, x_max = x[:, 0].min() -1, x[:, 0].max() +1
...     y_min, y_max = x[:, 1].min() -1, x[:, 1].max() +1
...     xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
...     z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
...     z = z.reshape(xx.shape)
...     plt.figure()
...     plt.pcolormesh(xx, yy, z, cmap=cmap_light)
...     plt.scatter(x[:, 0], x[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=24)
...     plt.xlim(xx.min(), xx.max())
...     plt.ylim(yy.min(), yy.max())
...     plt.title("AdaBoostRegressor (learning_rate='%s')" %(learning_rate))
...