Model selection | Regression variable selection
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (called the validation dataset or testing set). The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem). One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, in most methods multiple rounds of cross-validation are performed using different partitions, and the validation results are combined (e.g. averaged) over the rounds to give an estimate of the model's predictive performance. In summary, cross-validation combines (averages) measures of fitness in prediction to derive a more accurate estimate of model prediction performance. (Wikipedia).
In this video, we learn a hack to increase the size of our training set while still being able to do validation: cross validation. Link to my notes on Introduction to Data Science: https://github.com/knathanieltucker/data-science-foundations Try answering these comprehension questions to
From playlist Introduction to Data Science - Foundations
We go over ways to implement cross validation, and begin working on neural networks.
From playlist MachineLearning
How does cross validation performance change with increase in number of folds K?
#machinelearning #shorts #datascience
From playlist Quick Machine Learning Concepts
Let's talk about an important machine learning topic used to evaluate models: cross validation ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.lin
From playlist Machine Learning 101
10f Machine Learning: Cross Validation Considerations
Lecture on model cross validation, including workflows and philosophy.
From playlist Machine Learning
(ML 12.5) Cross-validation (part 1)
Description of K-fold cross-validation (CV), leave-one-out cross-validation (LOOCV), and random subsamples, for model selection.
From playlist Machine Learning
Cross Validation in Scikit Learn
This is the big one. We go over cross validation and other techniques to split your data. VERY IMPORTANT. We talk about cross validated scoring and prediction and then we talk about scikit learn cross validation iterators: K-fold, stratified fold, grouped data, and time series split. Asso
From playlist A Bit of Data Science and Scikit Learn
Cross-Validation In Machine Learning | ML Fundamentals | Machine Learning Tutorial | Edureka
*** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training *** This Edureka Video on 'Cross-Validation In Machine Learning' covers A brief introduction to Cross-Validation with its various types, limitations, and applications. Following are
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Statistical Learning: 6.5 Validation and cross validation
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 5.2 K-fold Cross Validation
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 5.3 Cross Validation the wrong and right way
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 5.R.1 Cross Validation
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Statistical Learning: 5.1 Cross Validation
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Approximate cross validation for large data and high dimensions - Tamara Broderick, MIT
The error or variability of statistical and machine learning algorithms is often assessed by repeatedly re-fitting a model with different weighted versions of the observed data. The ubiquitous tools of cross-validation (CV) and the bootstrap are examples of this technique. These methods a
From playlist Statistics and computation
Statistical Rethinking 2022 Lecture 07 - Overfitting
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music: Intro: https://www.youtube.com/watch?v=R9bwnY05GoU Pause: https://www.youtube.com/watch?v=wAPCSnAhhC8 Chapters: 00:00 Introduction 04:26 Problems of prediction 07:00 Cross-validation 22:00 Regula
From playlist Statistical Rethinking 2022
Stanford CS229: Machine Learning | Summer 2019 | Lecture 12 - Bias and Variance & Regularization
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3notMzh Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
Cross Validation : Data Science Concepts
All about the *very widely used* data science concept called cross validation.
From playlist Data Science Concepts
Statistical Learning: 5.5 More on the Bootstrap
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning