python-scikit-learnClustering example
from sklearn import datasets, cluster
X, y = datasets.load_iris(return_X_y=True)
model = cluster.KMeans(n_clusters = 3)
model.fit(X)
clusters = model.predict(X)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
.KMeans(create KMeans clustering model | n_clustersnumber of clusters we want to see |
.fit(train model with a given features and target variable dataset | .predict(predict target variable based on given features dataset |
Usage example
from sklearn import datasets, cluster
X, y = datasets.load_iris(return_X_y=True)
model = cluster.KMeans(n_clusters = 3)
model.fit(X)
clusters = model.predict(X)
print(clusters)output
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 2 0 0 0 0
0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0
0 2]
More of Python Scikit Learn
- How to get best parameters from grid search CV
- How to get best estimator from grid search CV
- Grid search CV example
- Using OneClassSVM for outliers detection example
- Using dbscan clustering example
- Use imputer for one column only
- Simple imputer example
- Using Ridge regression model example
- How to disable cross validation in grid search CV
- Yeo Johnson transformation example
See more codes...