[Day10] scikit-learn을 배워보자.
사이킷-런은 인공지능 구현을 위한 파이썬 패키지인데, 미리 준비된 데이타 셋을 제공하고 있어 연습용으로도 활용할 수 있고, 주요한 알고리즘도 미리 구현된 api를 제공한다. 버전 숫자가 낮다고 걱정하지 말자. 구글 개발자가 처음 만들기 시작한 패키지이며, 이미 오래전부터 널리 사용하고 있고 믿을 수 있다고 한다.
- 최적의 ‘머신러닝 알고리즘’을 고르기 위한 치트키: https://blogs.sas.com/content/saskorea/2017/08/22/%EC%B5%9C%EC%A0%81%EC%9D%98-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-%EC%95%8C%EA%B3%A0%EB%A6%AC%EC%A6%98%EC%9D%84-%EA%B3%A0%EB%A5%B4%EA%B8%B0-%EC%9C%84%ED%95%9C-%EC%B9%98%ED%8A%B8/
최적의 ‘머신러닝 알고리즘’을 고르기 위한 치트키
“어떤 알고리즘을 사용해야 할까요?
blogs.sas.com
- Reinforcement Learning KR: https://github.com/reinforcement-learning-kr
Reinforcement Learning KR
Reinforcement Learning KR has 18 repositories available. Follow their code on GitHub.
github.com
- Awesome Reinforcement Learning: https://github.com/aikorea/awesome-rl
aikorea/awesome-rl
Reinforcement learning resources curated. Contribute to aikorea/awesome-rl development by creating an account on GitHub.
github.com
- Choosing the right estimator: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
Choosing the right estimator — scikit-learn 0.23.2 documentation
Choosing the right estimator Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is desi
scikit-learn.org
- Installing scikit-learn: https://scikit-learn.org/stable/install.html
Installing scikit-learn — scikit-learn 0.23.2 documentation
Installing scikit-learn There are different ways to install scikit-learn: Install the latest official release. This is the best approach for most users. It will provide a stable version and pre-built packages are available for most platforms. Install the v
scikit-learn.org
- scikit-learn, Machine Learning in Python: https://scikit-learn.org/stable/index.html
scikit-learn: machine learning in Python — scikit-learn 0.23.2 documentation
Model selection Comparing, validating and choosing parameters and models. Applications: Improved accuracy via parameter tuning Algorithms: grid search, cross validation, metrics, and more...
scikit-learn.org
- Learning Scikit-Learn: youtu.be/rvVkVsG49uU
- Liz Sander - Software Library APIs: Lessons Learned from scikit-learn - PyCon 2018: youtu.be/WCEXYvv-T5Q
- API Reference: https://scikit-learn.org/stable/modules/classes.html
API Reference — scikit-learn 0.23.2 documentation
scikit-learn.org
- Mean squared error: https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-error
3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 0.23.2 documentation
3.3. Metrics and scoring: quantifying the quality of predictions There are 3 different APIs for evaluating the quality of a model’s predictions: Finally, Dummy estimators are useful to get a baseline value of those metrics for random predictions. 3.3.1.
scikit-learn.org
- Dataset loading utilities: https://scikit-learn.org/stable/datasets/index.html#datasets
7. Dataset loading utilities — scikit-learn 0.23.2 documentation
The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a m
scikit-learn.org
- sklearn.utils.Bunch: https://scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html?highlight=bunch#sklearn.utils.Bunch
sklearn.utils.Bunch — scikit-learn 0.23.2 documentation
scikit-learn.org
- sklearn.datasets.load_wine: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html?highlight=wine#sklearn.datasets.load_wine
sklearn.datasets.load_wine — scikit-learn 0.23.2 documentation
scikit-learn.org