# scikit meta: voting

Notes

To run the code yourself, you'll need to make sure the following tools are loaded.

``````import numpy as np
import matplotlib.pylab as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.datasets import make_classification
``````

The classification problem that we'll try to solve is generated.

``````X, y = make_classification(n_samples=2000, n_features=2,
n_redundant=0, random_state=21,
class_sep=1.75, flip_y=0.1)
plt.scatter(X[:, 0], X[:, 1], c=y, s=5);
``````

To get the pretty plots you'll need the following function.

``````def make_plots():
X_new = np.concatenate([
np.random.uniform(np.min(X[:, 0]), np.max(X[:, 0]), (20000, 1)),
np.random.uniform(np.min(X[:, 1]), np.max(X[:, 1]), (20000, 1))
], axis=1)
plt.figure(figsize=(16, 4))
plt.subplot(141)
plt.scatter(X[:, 0], X[:, 1], c=y, s=5)
plt.title("original data")
plt.subplot(142)
plt.scatter(X_new[:, 0], X_new[:, 1], c=clf1.predict_proba(X_new)[:, 1], s=5)
plt.title("ens1")
plt.subplot(143)
plt.scatter(X_new[:, 0], X_new[:, 1], c=clf2.predict_proba(X_new)[:, 1], s=5)
plt.title("ens2")
plt.subplot(144)
plt.scatter(X_new[:, 0], X_new[:, 1], c=clf3.predict_proba(X_new)[:, 1], s=5)
plt.title("ens3");
``````

To train the three models and get the visuals you can run:

``````clf1 = LogisticRegression().fit(X, y)
clf2 = KNeighborsClassifier(n_neighbors=10).fit(X, y)
clf3 = VotingClassifier(estimators=[('clf1', clf1), ('clf2', clf2)],
voting='soft',
weights=[10.5, 2.5])
clf3.fit(X, y)

make_plots()
``````

Make sure you play with the `weights` parameter to get a feel of the effect.

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