📑 Sklearn Workout 01. Multioutput Toy Regression

sklearn_01.html

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import sklearn as sl,pandas as pd,numpy as np,pylab as pl

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from sklearn.datasets import make_regression

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from sklearn.multioutput import MultiOutputRegressor

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from sklearn.ensemble import GradientBoostingRegressor

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Multioutput Toy Regression

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X,y=make_regression(

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    n_samples=1000,n_features=7,n_targets=3,random_state=1)

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pl.figure(figsize=(6,4))

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colors=['darkred','darkorchid','darkblue']

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markers=['h','v','s']

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m=100

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py=MultiOutputRegressor(

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    GradientBoostingRegressor(random_state=0))\

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    .fit(X,y).predict(X)

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for i in range(3):

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    pl.plot(X[:m,0],y[:m,i],'o',c=colors[i],

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            ms=7,marker=markers[i],alpha=.3,

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            label='y%d'%i)

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    pl.plot(X[:m,0],py[:m,i],'o',c=colors[i],

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            ms=3,marker=markers[i],alpha=.8,

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            label='py%d'%i)

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pl.grid(); pl.legend() 

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pl.tight_layout(); pl.show()

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