Scikit-Learn is possibly the most popular machine learning framework in the world. In this series of videos we'd like to give an overview of the main features and how you can use the framework to approach most machine learning problems. Do watch all the videos because we also want to highlight the dangers of it.
Let's add a preprocessing step by introducing it in a pipeline.
from sklearn.neighbors import KNeighborsRegressor from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_boston from sklearn.pipeline import Pipeline import matplotlib.pylab as plt X, y = load_boston(return_X_y=True) pipe = Pipeline([ ("scale", StandardScaler()), ("model", KNeighborsRegressor()) ]) pred = pipe.predict(X) plt.scatter(pred, y)
Note that this
pipe also has the same
.fit(X, y).predict(X) api.
Feedback? See an issue? Something unclear? Feel free to mention it here.
If you want to be kept up to date, consider getting the newsletter.