Kaggle's 30 Days Of ML (Day-13 Part-2): Cross-validation | Summary and Q&A

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August 14, 2021
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Abhishek Thakur
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Kaggle's 30 Days Of ML (Day-13 Part-2): Cross-validation

TL;DR

Cross validation is essential in machine learning to prevent model performance from being determined by luck, allowing for more robust evaluation and parameter optimization.

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Questions & Answers

Q: Why is cross validation important in machine learning?

Cross validation is important because it prevents model performance from being determined by luck. It allows for more robust evaluation by training and validating the model on different subsets of the data.

Q: What is K-fold cross validation?

K-fold cross validation involves splitting the data into k equal parts, training the model on k-1 parts, and validating it on the remaining part. This process is repeated k times, with each part serving as the validation set once.

Q: What is the purpose of stratified K-fold cross validation?

Stratified K-fold cross validation ensures that each fold has the same ratio of labels, thereby preventing biased evaluation. It is commonly used in classification problems to maintain consistent label distributions in each fold.

Q: How does leave-one-out cross validation work?

In leave-one-out cross validation, the model is trained on all data except one sample, which is used for validation. This process is repeated for each sample in the dataset, and the average performance is calculated.

Summary & Key Takeaways

  • Cross validation is a method used in machine learning to evaluate model performance by splitting the data into multiple sets for training and validation.

  • K-fold cross validation is a common approach where the data is divided into k equal parts, and the model is trained on k-1 parts and validated on the remaining part.

  • Stratified K-fold cross validation maintains the same ratio of labels in each fold to ensure unbiased evaluation, especially in classification problems. Leave-one-out and regression cross validation methods are also discussed.

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