sklearn datasets
时间: 2023-10-15 10:04:28 浏览: 56
The `sklearn.datasets` module provides several toy datasets and popular benchmark datasets for practicing machine learning algorithms in Python. Some of the popular datasets provided by `sklearn.datasets` include:
1. Iris Dataset: A dataset with 150 observations of iris flowers, with four features (petal length, petal width, sepal length, sepal width) and three classes (setosa, versicolor, virginica).
2. Boston Housing Dataset: A dataset with 506 observations of Boston housing prices, with 13 features describing various aspects of the housing environment.
3. Diabetes Dataset: A dataset with 442 observations of diabetes patients, with 10 features describing various aspects of their medical condition.
4. Breast Cancer Dataset: A dataset with 569 observations of breast cancer patients, with 30 features describing various aspects of the tumors.
5. MNIST Dataset: A dataset with 70,000 handwritten digits, split into a training set of 60,000 and a test set of 10,000.
6. Wine Dataset: A dataset with 178 observations of wines, with 13 features describing various aspects of the wines.
7. Olivetti Faces Dataset: A dataset with 400 images of 40 individuals, with 64 pixel by 64 pixel grayscale images.
These datasets can be easily loaded using various functions provided by the `sklearn.datasets` module. For example, the Iris dataset can be loaded using the following code:
```
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
```
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