rbklion.blogg.se - Caret models

CARET MODELS HOW TO

In this brief tutorial, we will go through an example from start to finish. It provides: tools for splitting your data into training and test sets, a number of different models, from random forests to logistic regression, and tools for selecting the best model out of a set of alternatives. The best package for doing this in R is caret (short for Classification And REgression Training). In fact, often times, when you want to optimize on prediction accuracy, it is worth trying a range of other methods to compare which provides the most accurate predictions. That said, there are no guarantees that it will be the most accurate method for predicting your outcome of interest. Linear regression will be useful in a wide range of situations, particularly for descriptive and scientific purposes when you want outputs that you can interpret.

CARET MODELS HOW TO

10.2 Producing a skip-gram matrix for semantic network analysis and embedding modelsĪ few weeks back, we learned how to perform linear regression, with single and multiple predictors.

10.1 Understanding network data structures.

10 Social and Semantic Network Analysis.

8.3 Linear regression using the lm function.

8 Machine Learning I: Linear and Multiple Regression.

6 Surveys and Survey Experiments with Qualtrics.

1.13 December 11th: Final papers are due.

November 8th): Semantic Change and Historical Meaning (Lab: Semantic Network Analysis)

November 1st): Homophily and Diffusion (Lab: Network Analysis) October 25th): Discrimination (Lab: Machine Learning) October 11th): Polarization (Lab: Analyzing Text) October 4th): Inequality (Lab: Collecting Data Online) September 27th): Ethics (Lab: Surveys and Survey Experiments) September 20th): Introductions (Lab: Introduction to R)