tailieunhanh - Lecture Applied data science: Validation

Lecture "Applied data science: Validation" includes content: validation set approach; overfitting; cross-validation; data leakage; nested cross-validation; bootstrapping; . We invite you to consult! | Validation Overview 1. Introduction 8. Validation 2. Application 9. Regularisation 3. EDA 10. Clustering 4. Learning Process 11. Evaluation 5. Bias-Variance Tradeoff 12. Deployment 6. Regression review 13. Ethics 7. Classification Lecture outline - Validation set approach - Overfitting - Cross-validation - Data leakage - Nested cross-validation - Bootstrapping Validation set approach Validation set approach - Randomly split the original data into two a training set and a test set. - Fit the OLS model on the training set and predict the responses in the validation set - Calculate the test MSE MSE from using the model on the test set Validation set approach Overfitting is the tendency of data mining procedures to tailor models to the training data at the expense of generalisation to previously unseen data. as a model gets more complex it is allowed to pick up harmful false correlations noise . The harm occurs when these false correlations produce incorrect generalisations in the model. Validation set approach continued Pros. simple and easy to implement Cons. - Highly variable in multiple runs - Tend to overestimate the test error because we used only roughly half of the original dataset for training Cross-validation Cross-validation is a resampling method which - Repeatedly and randomly draws subsets of data from a sample - Refits a model . OLS on these subsets of data to reveal information unknown if fitted the model only once . variability of the fitted model - Is computationally expensive - Has 2 common methods - Cross-validation model selection and model evaluation - Bootstrapping evaluating the variability of a parameter estimate Leave one out cross validation LOOCV - Use only 1 observation for testing and fit the OLS regression on the remaining of the original data - Repeat the procedure n times so that each observation is used for testing once. - Calculate CV error Leave one out cross validation LOOCV Pros - Unbiased estimate of the test error because

• Cơ sở dữ liệu
• Lecture Applied data science
• Applied data science
• Validation set approach
• Cross validation
• Nested cross validation
• Data leakage
• International journal of computer science & communication networks
• Cluster based data distribution techniques for leakage detection
• Data distribution techniques
• Done data leakage
• Share the data
• Data leakage detection
• The original distributor’s data
• Data recipient is malicious
• Objects or records
• Techniques for detecting leakage
• Cloud computing
• Data outsourcing
• Access control
• Inference leakage
• Presence of the inference problem
• Unidirectional data transfer
• Industrial control system
• Government agencies
• Optical fiber
• Avoiding leakage sensitive information
• Pseudorandom sequences classification algorithm
• Statistical data analysis
• Machine learning
• Classification of binary sequences
• DLP systems
• Information leakage protection