python-scikit-learnHow to get model homogeneity score
from sklearn import datasets, neighbors, model_selection, metrics
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = neighbors.KNeighborsClassifier(3)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
hs = metrics.homogeneity_score(y_pred, y_test)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
model_selection.train_test_splitsplits given | .fit(train model with a given features and target variable dataset |
.predict(predict target variable based on given features dataset | .homogeneity_score(calculate homogenity score based on model predictions and actual values |
Usage example
from sklearn import datasets, neighbors, model_selection, metrics
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = neighbors.KNeighborsClassifier(3)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
hs = metrics.homogeneity_score(y_pred, y_test)
print(hs)output
0.9153422763708112
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