Statistical & Data Science Modelling in High Dimension
1,123 views · Published 25 September 2017 · 12:10 · Indexed 22 September 2026
Channel: AnalyticsU · 2017 · Education
Statistical modelling in high dimension : When there are too many predictors variables, selecting the best set is a challenge. There are ways to reduce the dimension.Following ways can be tried out. 1- feature Selection 2- Shrinkage (Regularization) 3- Dimension reduction (PCA & PLS) A number of points have to be kept in mind while dealing with high dimension data ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
