cross validation techniques in machine learning | hyperparameter optimization series part4

Pubblicato il: 08 novembre 2023
sul canale di: Python Odyssey தமிழ்

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what is cross-validation, and why should you care about it and what are the different cross validation techniques available in machine learning ?

In the realm of data-driven decision-making, it's important to recognize the challenges that arise when working with limited data. One such challenge is the dilemma of partitioning data into training and validation sets, which can significantly impact the outcomes and generalization of machine learning models. The results can even be influenced by the random selection of these sets, leading to potential bias.

However, there's a powerful solution to this problem known as cross-validation, often abbreviated as CV. Cross-validation redefines how we approach model training and validation, allowing us to harness the full potential of our data without the constraints of arbitrary set selections.

In the following minutes, we'll break it down into simple terms and show you the different cross validation techniques that are available

00:00 what is cross validation
00:40 cross validation for model evaluation
02:34 cross validation for model hyperparameter tuning
04:21 Hold-out cross-validation.
05:04 K-fold cross-validation.
07:30 Leave-one-out cross-validation
12:24 Stratified k-fold cross-validation.

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Hold-out cross-validation, K-fold cross-validation, Leave-one-out cross-validation, Stratified k-fold cross-validation.

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