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# Class Probabilities Calculated by the VotingClassifier in Scikit-learn

Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the VotingClassifier.

First, three examplary classifiers are initialized (LogisticRegression, GaussianNB, and RandomForestClassifier) and used to initialize a soft-voting

VotingClassifier with weights [1, 1, 5], which means that the predicted probabilities of the RandomForestClassifier count 5 times as much as the weights of the other classifiers when the averaged probability is calculated.

To visualize the probability weighting, we fit each classifier on the training set and plot the predicted class probabilities for the first sample in this example dataset.

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### Version¶

In [1]:
import sklearn
sklearn.__version__

Out[1]:
'0.18.1'

### Imports¶

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier

Automatically created module for IPython interactive environment


### Calculations¶

In [3]:
clf1 = LogisticRegression(random_state=123)
clf2 = RandomForestClassifier(random_state=123)
clf3 = GaussianNB()
X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([1, 1, 2, 2])

eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],
voting='soft',
weights=[1, 1, 5])

# predict class probabilities for all classifiers
probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]

# get class probabilities for the first sample in the dataset
class1_1 = [pr[0, 0] for pr in probas]
class2_1 = [pr[0, 1] for pr in probas]


### Plot Results¶

In [4]:
N = 4  # number of groups

x_axis = ['LogisticRegression<br>weight 1',
'GaussianNB<br>weight 1',
'RandomForestClassifier<br>weight 5',
'VotingClassifier<br>(average probabilities)'
]

# bars for classifier 1-3
p1 = go.Bar(x=x_axis, y=np.hstack(([class1_1[:-1], [0]])),
marker=dict(color='green'),
name='class1'
)

p2 = go.Bar(x=x_axis, y=np.hstack(([class2_1[:-1], [0]])),
marker=dict(color='lightgreen'),
name='class2'
)

# bars for VotingClassifier
p3 = go.Bar(x=x_axis, y=[0, 0, 0, class1_1[-1]],
marker=dict(color='blue'),
showlegend=False
)
p4 = go.Bar(x=x_axis, y=[0, 0, 0, class2_1[-1]],
marker=dict(color='steelblue'),
showlegend=False
)

layout = go.Layout(title='Class probabilities for sample 1 by different classifiers')

fig = go.Figure(data=[p1, p2, p3, p4], layout=layout)

In [5]:
py.iplot(fig)

Out[5]:
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