Скачать или смотреть 4. Splitting Data for Machine Learning: Train-Test & Validation Sets 📊

Welcome back to the Machine Learning Classification series! After performing Exploratory Data Analysis (EDA), the next crucial step is splitting the dataset to ensure a fair evaluation of our model. In this video, we’ll discuss how to properly divide data into training, validation, and test sets.

What You’ll Learn:
✅ Why splitting data is important in Machine Learning
✅ How to split data into train, validation, and test sets
✅ Using train_test_split() from Scikit-Learn
✅ Ensuring balanced class distributions (stratification)
✅ Avoiding data leakage for reliable model evaluation

By the end of this video, you’ll know how to correctly split your dataset, ensuring your model generalizes well to new data.

📌 Useful Links
📊 Dataset Links:
📌 Heart Disease Dataset 1: https://www.kaggle.com/datasets/johns...
📌 Heart Disease Dataset 2: https://www.kaggle.com/datasets/deeks...

🗺️ Machine Learning Workflow Map:
📌 Scikit-Learn ML Map: https://scikit-learn.org/stable/machi...

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#MachineLearning #TrainTestSplit #DataSplitting #DataScience #MLforBeginners #LearnMachineLearning #AI #ScikitLearn #PythonProgramming #MLModels #DataAnalytics #CodingTutorial