Thanks for watching my video! Want to learn more about Machine Learning? Check out my 30+ video playlist on Python & scikit-learn here: ua-cam.com/play/PLTsu3dft3CWhSJh3x5T6jqPWTTg2i6jp1.html&si=UOuvLD9dFqWtiN1T
Thank you always for your tutorial videos! Are random forest classifiers one of the simplest machine learning techniques? In your opinion, what is the order of complexity for machine learning techniques? I've seen several applications; however random forest classifier was used in the Kaggle titanic competition where the submitter received 100% prediction accuracy. Thanks again Onur!
Random Forest isn’t the simplest machine learning method, but it’s not overly complicated either. It works by combining multiple decision trees to make better predictions, which makes it more accurate but also a bit more resource-intensive. Simpler methods like linear regression or k-NN are easier to use, while more advanced ones, like neural networks, are much more complex. As for the Kaggle Titanic competition, 100% accuracy is likely because the dataset is small and well-structured, or due to clever feature engineering. In real life, perfect accuracy is unusual and often means the model is overfitting. Thanks for watching!
Thanks for watching my video! Want to learn more about Machine Learning? Check out my 30+ video playlist on Python & scikit-learn here: ua-cam.com/play/PLTsu3dft3CWhSJh3x5T6jqPWTTg2i6jp1.html&si=UOuvLD9dFqWtiN1T
Good one
Thanks!
Thank you always for your tutorial videos!
Are random forest classifiers one of the simplest machine learning techniques?
In your opinion, what is the order of complexity for machine learning techniques? I've seen several applications; however random forest classifier was used in the Kaggle titanic competition where the submitter received 100% prediction accuracy.
Thanks again Onur!
Random Forest isn’t the simplest machine learning method, but it’s not overly complicated either. It works by combining multiple decision trees to make better predictions, which makes it more accurate but also a bit more resource-intensive. Simpler methods like linear regression or k-NN are easier to use, while more advanced ones, like neural networks, are much more complex. As for the Kaggle Titanic competition, 100% accuracy is likely because the dataset is small and well-structured, or due to clever feature engineering. In real life, perfect accuracy is unusual and often means the model is overfitting. Thanks for watching!