Using Linear Regression for Predictive Modeling in R

@machinelearnbot

Predictive models are extremely useful for forecasting future outcomes and estimating metrics that are impractical to measure. For example, data scientists could use predictive models to forecast crop yields based on rainfall and temperature, or to determine whether patients with certain traits are more likely to react badly to a new medication. Before we talk about linear regression specifically, let's remind ourselves what a typical data science workflow might look like. A lot of the time, we'll start with a question we want to answer, and do something like the following: Linear regression is one of the simplest and most common supervised machine learning algorithms that data scientists use for predictive modeling. In this post, we'll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.


The Grammar of Data Science: Python vs R

#artificialintelligence

The main issue here is that the model fit, not the actual data, controls the scale of the y-axis. The model fits dwarf the actual data! In R, ggplot automatically solves this problem for you, which makes the visualization useful out of the box without further effort. Furthermore, I find the R code easier to write than the Python code because it is composed of a combination of simple and easy to remember elements, e.g., geoms. It reads like English, and allows me to fit arbitrarily complex models to the data using the formula syntax.


Linear Regression

#artificialintelligence

Predictive models are extremely useful for forecasting future outcomes and estimating metrics that are impractical to measure. For example, data scientists could use predictive models to forecast crop yields based on rainfall and temperature, or to determine whether patients with certain traits are more likely to react badly to a new medication. Before we talk about linear regression specifically, let's remind ourselves what a typical data science workflow might look like. A lot of the time, we'll start with a question we want to answer, and do something like the following: Linear regression is one of the simplest and most common supervised machine learning algorithms that data scientists use for predictive modeling. In this post, we'll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure. This post is part of our focus on nature data this month.


R packages for summarising data – part 2

@machinelearnbot

This does only work on numeric variables, but the summary it produces is extremely comprehensive. Certainly every summary statistic I normally look at for numeric data is shown somewhere in the above! The output it produces is very clear in terms of human readability, although it doesn't work directly with kable, nor does it produce tidy data if you wished to use the results downstream. One concern I had regards its reporting of missing data. The above output suggests that my data$score field has 58 entries, and 0 missing data. In reality, the dataframe contains 64 entries and has 6 records with a missing (NA) score.


The Landscape of R Packages for Automated Exploratory Data Analysis

arXiv.org Machine Learning

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of twelve popular R packages to identify the parts of analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development.