PYTHON SKLEARN - MODEL SELECTION : Train_test_split, Cross Validation, GridSearchCV (21/30)
This French Python tutorial introduces you to SKLEARN, the best package for machine learning with Python. With Sklearn, you can split your dataset into a Train_set and a Test_set using the Train_test_split function. This function is very important and should be used for every machine learning project, even before developing a model with Sklearn. You can define the proportions for splitting your dataset with the test_size = ... argument. Once you've used train_test_split, you can train your model and validate it using cross-validation. It's best to use the GridSearchCV class, but the cross_val_score, Validation_curve, and Learning_curves functions are also very useful. ► VIDEO TIMECODE: 0:00 Intro 0:39 Train_test_split 04:22 Set Validation 07:05 Cross Validation 09:44 Curve Validation 12:46 GridSearchCV 16:04 Learning Curves 19:26 Titanic Exercise ► Support me financially on Tipeee (and get BONUS videos) https://fr.tipeee.com/machine-learnia ► JOIN OUR DISCORD COMMUNITY / discord ► MY WEBSITE: https://www.machinelearnia.com/ ► Get my free book: LEARN MACHINE LEARNING IN ONE WEEK CLICK HERE: https://www.machinelearnia.com/appren... ► Download my code for free on GitHub: https://github.com/MachineLearnia ► Subscribe: / @machinelearnia ► Who am I? I'm Guillaume Saint-Cirgue, a Senior Data Scientist with over 8 years of experience in the tech, aviation, robotics, energy, and smart factory sectors. In 2019, I founded Machine Learnia to share my knowledge in the field of artificial intelligence. My goal is to explain in detail how machine learning and its algorithms work, while making these concepts accessible to everyone. I firmly believe that simply skimming over the mathematical aspects of this field is not enough; It's essential to dive deep into it to stand out. This approach has already convinced more than 150,000 people, and those I train today are among the best in the industry. ► Any questions? Contact me: [email protected]
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