Iris dataset
Dataset概要
- For supervised learning, classification (three-classes)
- target (classes) : setosa(0), versicolor(1), virginica(2)
- feature : sepal length (cm), sepal width (cm), petal length (cm), petal width (cm)
Technical Info
- import : from sklearn.datasets import load_iris
- dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])
- iris_dataset['data'].shape : (150, 4)
- iris_dataset['target'].shape : (150,)
取り上げている書籍
- [Introduction to Machine Learning with Python]
使われ方例
k-nearest neighbors classification algorithm
X_train, X_test, y_train, y_test = train_test_split(
iris_dataset['data'], iris_dataset['target'], random_state=0)
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)
print("Test set score: {:.2f}".format(knn.score(X_test, y_test)))
Wisconsin Breast Cancer dataset
Dataset概要
Technical Info
- cancer.keys():
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])
Boston Housing dataset
Dataset概要
Technical Info
