Análisis de datos de la EPH en R
Finalmente disponible la versión en español del _Data Science Live Book_! El libro se abre sin barreras idiomáticas ante las personas de habla-hispana con ganas de aprender 👨🎓👩🎓. Esta publicación es una edición revisada tanto en gramática como en aspectos técnicos de la versión en inglés.
Opening the black-box in complex models: SHAP values. What are they and how to draw conclusions from them? With R code example!
This method can discretize a variable taking into consideration the target variable, similar to what decision tree do but with gain ratio.
From a gentle introduction to a practical solution, this is a post about feature selection using genetic algorithms in R.
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Scenario: we got a table of id-value, and a matrix/tibble that contains the id, and we need the labels. It may be useful when predicting the Key (or Ids) of in a
This tutorial will introduce the Deep Learning classification task with Keras. With focus on one-hot encoding, layer shapes, train & model evaluation.
Analyzing the relationship between the sample size and how it impacts on the accuracy in a classification model
tl;dr: This post is related to How to self-publish a book: A handy list of resources. It's centered around Bookdown and some non-standard customizations I found useful to create the Data Science
tl;dr: A list of useful resources aimed to self-publish a book on Amazon using Bookdown.
Exploratory data analysis (EDA) the very first step in a data project. We will create a code-template to achieve this with one function.
Este tutorial tiene como propósito hacer el set-up inicial para empezar a desarrollar modelos machine learning en increíble lenguaje R.
About Machine Learning We all know that machine learning is about handling data, but it also can be seen as: The art of finding order in data by browsing its inner information. Some
Well, what you hate is the way that math was taught to you. That soup of equations, abstractions, and solutions to problems that we don’t know, It's hard to enjoy the things
Hi there! tl;dr: The Data Science Live Book is now available at Amazon! Kindle & Paperback versions! 🚀 👉 See at Amazon 📗! Link to the black & white version, also available on full-color. It
funModeling quick-start This package contains a set of functions related to exploratory data analysis, data preparation, and model performance. It is used by people coming from business, research, and teaching (professors and students)
Amazon Redshift is one of the hottest databases for Data Warehousing right now, it's one of the most cost-effective solutions available, and allows for integration with many popular BI tools. Unfortunately, the status
tl;dr: Convert numerical variables into categorical, as it is shown in the next image. ⏳ Reading time ~ 6 min. Let's start! The package funModeling (from version > 1.6.6) introduces two functions—
This package lets you analyze the variables of a dataset, to evaluate how the data is shaped. Consider this the first step when you have your data for modeling, you can use this
Well after some time, and +300 commits, this is the biggest release of the Data Science Live Book! (open source), after the first publication more than 1 year ago :) tl;dr: Hi there!
Playing with dimensions Hi there! This post is an experiment combining the result of t-SNE with two well known clustering techniques: k-means and hierarchical. This will be the practical section, in R. But
I have a new year's surprise for you! This shiny app means to be a system for basic reporting in the style of most Business Intelligence tools, you can create a report without
A year ago i wrote about a way to authenticate shiny with Auth0, using Apache: http://blog.datascienceheroes.com/adding-authentication-to-shiny-open-source-edition/ This method works but has some issues, Sebastian Peyrott has written an excellent
Hi there! I decided to almost re-write the model validation section since it didn't reflect real case scenarios. Hopefully in the two new chapters you will gain a deeper knowledge on methodological aspects