scikit prep.

The way data is preprocessed can have a huge effect in your scikit-learn pipelines. This series of videos will highlight common techniques for preprocessing data for modelling.

Introduction Standard Quantile Transform Predict Poly OneHot Drawdata

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Notes

You can download the dataset here.

Here's what the original dataset looks like.

df = pd.read_csv("drawndata2.csv")
X = df[['x', 'y']].values
y = df['z'] == 'a'
plt.scatter(X[:, 0], X[:, 1], c=y);

Here's the PolynomialFeatures at work.

from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline

pipe = Pipeline([
    ("scale", PolynomialFeatures()),
    ("model", LogisticRegression())
])

pred = pipe.fit(X, y).predict(X)
plt.scatter(X[:, 0], X[:, 1], c=pred);

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