In this post, you wil learn about how to use Sklearn datasets for training machine learning models.
Here is a list of different types of datasets which are available as part of sklearn.datasets
- Iris (Iris plant datasets used – Classification)
- Boston (Boston house prices – Regression)
- Wine (Wine recognition set – Classification)
- Breast Cancer (Breast cancer wisconsin diagnostic – Classification)
- Digits (Optical recognition of handwritten digits dataset – Classification)
- Linnerud (Linnerrud dataset – Classification)
- Diabetes (Diabetes – Regression)
from sklearn import datasets iris = datasets.load_iris() boston = datasets.load_boston() breast_cancer = datasets.load_breast_cancer() diabetes = datasets.load_diabetes() wine = datasets.load_wine() datasets.load_linnerud() digits = datasets.load_digits()All of the datasets come with the following and are intended for use with supervised learning:
- Data (to be used for training)
- Labels (Target)
- Labels attriibute
- Description of the dataset
# Let's use IRIS as an example for reading different aspects of data iris.data iris.target iris.target_names print(iris.DESCR)
Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.
He has also authored the book, Building Web Apps with Spring 5 and Angular.
He has also authored the book, Building Web Apps with Spring 5 and Angular.
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