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Didactic tubes de CAS R.R. 1 Theme Model deployment with R using the file hash package. Model deployment is the last task of the data mining steps. It corresponds to several aspects e.g. generating

There are several types of predictive modeling techniques. The most common types are classifiers (the models used for classification); clustering algorithms (the models used for clustering); and models based on anomaly detection (the models used for anomaly detection). All those models depend on the structure of the data (e.g., training dataset) and some common assumptions about the data (e.g., structure of the data, how data is distributed, etc). This tutorial will introduce the context of predictive modeling with R. We start to learn the basic concepts around predictive modeling with R and start to build a working model with the same model and prediction results using the hash package. 1.2. Basic terminology In this series of tutorials, as in previous ones, R's terminology will be used throughout. Let's start out with some basic terminology. In this series, I will be using the term model as a general term for a class of prediction models, where they are presented (for example), in the following way: a) as a mathematical object. The data are fed into the mathematical object, which models the data. b) as an interface between users and the mathematical objects. The mathematical objects provide an interface on the user side which allows predictions. The user can interact with the interface to obtain predictions. 2. Data exploration I started out learning about the context of predictive modeling by doing this: cat DRESG.log | head -n 5 | large -n 500 read | head -n 1 | sort -n | sort -n | head -n 2 | large -n 100 -h | head -n 1 | sort -n | sort -n | head -n 2 | large -n 100 -w | head -n 1 | sort -n | sort -n | head -n 6 | large -n 100 -a | sort -n 3.1. Models used by model selection We used several of our data mining models to do prediction model selection. In the context of predictive modeling, there are two types of models: a) linear models (e.g., regression analysis); b) nonlinear and semi-loglinear models (e.g., classification analysis). We will start out learning about linear models by doing this: cat model_selection.

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