"""
KNN - k-nearest neighbor classifier
Lazy learning algorithm
Quick adaptation to new test data
Linear increase in computational complexity
"""
from sklearn.neighbors import KNeighborsClassifier
from iris_common_funcs import plot_decision_regions, initializer
import matplotlib.pyplot as plt
X_train_std, y_train, X_combined_std, y_combined = initializer()
knn = KNeighborsClassifier(n_neighbors=5, p=2,
metric='minkowski').fit(X_train_std, y_train)
'''
minkowski distance metric is a generalisation of the Euclidean and Manhattan distance metrics
At p=2, Euclidean distance is used and at p=1, the distance is Manhattan.
'''
plot_decision_regions(X=X_combined_std,
y=y_combined,
classifier=knn,
test_idx=range(105, 150))
plt.xlabel("Sepal length (standardized)")
plt.ylabel("Petal length (standardized)")
plt.legend(loc='upper left')
plt.tick_params(top=True, right=True)
plt.show()
"""
Decision Tree built using skikit learn and the iris dataset
"""
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from iris_common_funcs import plot_decision_regions, initializer
import matplotlib.pyplot as plt
x_train, y_train, x_combined, y_combined = initializer('val')
tree = DecisionTreeClassifier(criterion='entropy', max_depth=3,
random_state=0).fit(x_train, y_train)
plot_decision_regions(x_combined,
y_combined,
classifier=tree,
test_idx=range(105, 150))
plt.xlabel("Sepal length [cm]")
plt.ylabel("Petal length [cm]")
plt.legend(loc='upper left')
plt.show()
export_graphviz(tree,
out_file='iris_tree.dot',
feature_names=['petal length', 'petal width'])