so when you move caret rightwards you caret stops at these locations: 3|4|5|6|.In Sciter1 I am using "directional" caret approach – physical caret locations are dependent from direction from where caret arrive to that position: In flat models where text is just a sequence of "styled characters" this is probably not a major issue but in HTML WYSIWYG something needs to be done I think.
That kind of problme is typical for WYSIWYG editing implementations. Problem here as we can see: single caret position (visual) actually represents at least three physical DOM insertion points. Now consider these tasks, insert text "AB" in that paragraph between ‘4’ and ‘5’ so it will go in these locations: So they follow approach used by WYSIWYG editors like Word and others. Question is: how many caret positions in this paragraph?Īll known contenteditable implemenatations will give you 7 caret positions here: before 1, between 1 and 2, … after 7. That change requires me to rethink concept of caret positions again. The behavior:richtext is the thing behind Sciter’s element or in Web browsers.īehavior:richtext in Sciter1 uses “flat” DOM model: div:element, paragraph:element, that model is similar to the RichTextBox in Windows.īut in Sciter2 new behavior:richtext uses standard DOM model of HTML content: element:node, text:node, comment:node. These HTML pages were created using bookdown.Working on behavior:richtext again. You can always email me with questions,comments or suggestions. DataCamp has a beginner’s tutorial on machine learning in R using caret.At useR! 2014, I was interviewed and discussed the package and the book.There is a webinar for the package on Youtube that was organized and recorded by Ray DiGiacomo Jr for the Orange County R User Group.The example data can be obtained here(the predictors) and here (the outcomes). There is also a paper on caret in the Journal of Statistical Software.It is on sale at Amazon or the the publisher’s website. The book Applied Predictive Modeling features caret and over 40 other R packages.The current release version can be found on CRAN and the project is hosted on github. The package started off as a way to provide a uniform interface the functions themselves, as well as a way to standardize common tasks (such parameter tuning and variable importance). Some have different syntax for model training and/or prediction. There are many different modeling functions in R. The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. 22.2 Internal and External Performance Estimates.22 Feature Selection using Simulated Annealing.21.2 Internal and External Performance Estimates.21 Feature Selection using Genetic Algorithms.20.3 Recursive Feature Elimination via caret.20.2 Resampling and External Validation.If the character does not have an HTML entity, you can use the decimal (dec) or hexadecimal (hex) reference. 19 Feature Selection using Univariate Filters If you want any of these characters displayed in HTML, you can use the HTML entity found in the table below.18.1 Models with Built-In Feature Selection.16.6 Neural Networks with a Principal Component Step.16.2 Partial Least Squares Discriminant Analysis.16.1 Yet Another k-Nearest Neighbor Function.13.9 Illustrative Example 6: Offsets in Generalized Linear Models.13.8 Illustrative Example 5: Optimizing probability thresholds for class imbalances.13.7 Illustrative Example 4: PLS Feature Extraction Pre-Processing.13.6 Illustrative Example 3: Nonstandard Formulas.13.5 Illustrative Example 2: Something More Complicated - LogitBoost.13.2 Illustrative Example 1: SVMs with Laplacian Kernels.12.1.2 Using additional data to measure performance.12.1.1 More versatile tools for preprocessing data.11.4 Using Custom Subsampling Techniques.7.0.27 Multivariate Adaptive Regression Splines.5.9 Fitting Models Without Parameter Tuning.5.8 Exploring and Comparing Resampling Distributions.5.7 Extracting Predictions and Class Probabilities.5.1 Model Training and Parameter Tuning.4.4 Simple Splitting with Important Groups.4.1 Simple Splitting Based on the Outcome.3.2 Zero- and Near Zero-Variance Predictors.
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