cross validation in machine learning | hyper parameter optimization Part 3

Pubblicato il: 05 novembre 2023
sul canale di: Python Odyssey தமிழ்
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what is cross-validation, and why should you care about it?

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 how this technique can used for hyper parameter tuning and model evaluation & selection.

00:00 recap of hyperparameter and validation set
00:40 Problem in generating validation set
01:40 Cross validation introduction
02:05 K fold in cross validation
03:05 Use case introduction to train model using cross validation
04:40 What is present in folds ?
06:28 Train DT & Evaluate hyper parameter depth 10 using cross validation
11:24 cross validation accuracy
12:55 Train DT & Evaluate hyper parameter depth 15 using cross validation
13:40 Train DT & Evaluate hyper parameter depth 20 using cross validation
14:30 compare & pick best hyperparameter using cross validation accuracy
16:00 Building final model using train set and best hyperparameter value
18:10 Evaluating final model using test set

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