def draw_boundaries():
data = datasets.load_iris().data[:, :
2] # we only take the first two features.
label = np.array(datasets.load_iris().target,
dtype=int) # Take classes names
data = StandardScaler().fit_transform(data) # Rescale data
viz = DecisionViz(
GaussianNB(),
title="Gaussian",
features=['Sepal Length', 'Sepal Width'],
classes=['A', 'B', 'C'] # Determine dimension and no. of classes
)
viz.fit(data, label) # Train data to draw
viz.draw(data, label) # Draw Data
viz.show()
from sklearn.model_selection import train_test_split as tts
from sklearn.preprocessing import StandardScaler
from sklearn import datasets
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from yellowbrick.contrib.classifier import DecisionViz
iris = datasets.load_iris()
labels = pd.DataFrame(iris.target)
labels.columns = ['labels']
data = pd.DataFrame(iris.data)
data.columns = ['Sepal length', 'Sepal width', 'Petal length', 'Petal width']
data = pd.concat([data, labels], axis=1)
data_sp = data[['Sepal length', 'Sepal width', 'labels']]
X = StandardScaler().fit_transform(data_sp.drop(['labels'], axis=1))
X_train, X_test, y_train, y_test = tts(X,
data_sp['labels'],
test_size=.4,
random_state=42)
viz = DecisionViz(KNeighborsClassifier(3),
title="Nearest Neighbors",
features=['Feature One', 'Feature Two'],
classes=["0", "1", "2"])
viz.fit(X_train, y_train)
viz.draw(X_test, y_test)
viz.show()