scikit learn.

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.

Introduction Data Models Scale Pipeline Settings Grid Search Ouch Reality

Prev Video

Next Video

Episode Notes

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? Feel free to mention it here.

If you want to be kept up to date, consider getting the newsletter.