This notebook contains the code for the walkthrough in the quickstart guide. We've purposefully omitted the text of the guide so that you can follow along in code using this notebook as a template! The scikit-yb developers also use this notebook to verify that the quickstart code is correct, so if this code doesn't match what's on the guide, please leave us a note on our GitHub Issues.
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%matplotlib notebook
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import pandas as pd
from yellowbrick.datasets import load_bikeshare
X, y = load_bikeshare()
print(X.head())
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from yellowbrick.features import Rank2D
visualizer = Rank2D(algorithm="pearson")
visualizer.fit_transform(X)
visualizer.show()
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from yellowbrick.features import JointPlotVisualizer
visualizer = JointPlotVisualizer(feature='temp', target='feelslike')
visualizer.fit_transform(X['temp'], X['feelslike'])
visualizer.show()
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from yellowbrick.regressor import ResidualsPlot
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.1
)
visualizer = ResidualsPlot(LinearRegression())
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.show()
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import numpy as np
from sklearn.linear_model import RidgeCV
from yellowbrick.regressor import AlphaSelection
alphas = np.logspace(-10, 1, 200)
visualizer = AlphaSelection(RidgeCV(alphas=alphas))
visualizer.fit(X, y)
visualizer.show()
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from sklearn.linear_model import Ridge
from yellowbrick.regressor import PredictionError
visualizer = PredictionError(Ridge(alpha=3.181))
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.show()