Articles, Blog Learning Scikit-Learn (AI Adventures) Related posts: Kubeflow: Machine Learning on Kubernetes (AI Adventures) Machine Learning Meets Fashion (AI Adventures) How to Make an Image Classifier – Intro to Deep Learning #6 How to Make a Text Summarizer – Intro to Deep Learning #10 ai ai adventures cloud developers data science Dataset loading and manipulation GCP Google Cloud Platform Google Summer of Code intro to scikit-learn Kaggle kernel machine learning machine learning algorithms machine learning developers machine learning models ML ML algorithms ML library python python machine learning library scikit-learn support vector classifier SVC train_test_split Training models what is scikit-learn? Yufeng Guo Post navigation How academic freedom strengthens the bonds of accumulated knowledge | Nicholas ChristakisLearning styles & the importance of critical self-reflection | Tesia Marshik | TEDxUWLaCrosse 10 Comments SaiChandar Reddy Tarugu July 31, 2018 at 11:41 pm Reply Can I use metrics.accuracy_score to measure the accuracy of dataset.If not then how to measure accuracy.can you help me. Nick Kartha August 1, 2018 at 3:44 am Reply train_test_split is really neat 🙂 Filipe Silva August 1, 2018 at 1:31 pm Reply Your eyebrows are not coherent with your enthusiasm, but good content! Rachel Harrison August 1, 2018 at 8:32 pm Reply This series is super helpful, thanks guys! SaiChandar Reddy Tarugu August 7, 2018 at 8:38 am Reply How to choose k value in knn.Will it be based on the accuracy or square root of size of test data.Can anyone help me. Chris Keo September 2, 2018 at 7:15 am Reply Yufeng you beast! Good stuff! Amr Del January 31, 2019 at 8:58 pm Reply can we use an adaboost classifier with SVM as its weak learner with this scikit learn because we read some papers argumenting of the efficiency of adaboost with svm weaklearners better than the adaboost with decision stumps or others ?? Rudhisundar Beura February 4, 2019 at 2:38 am Reply Thank you, Yufeng! Abdillah Mohamed March 19, 2019 at 7:28 am Reply I am new in machine learning and now I am facing an issue. I have 7 projects I would like to predict whether a pull request would be rejected or not (Yes or No). And I would like to build a prediction model by using data from 6 projects as source project and predict the rejection of the pull request in the seventh project as a target project. Can you please tell me how can I structure my algorithm in Scikit-learn? Hope that my question is clear.Thanks kotov dot in July 24, 2019 at 2:23 pm Reply Scikit site is cool. For else, ~ 2:30 Leave a Reply Cancel Save my name, email, and website in this browser for the next time I comment.