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The Blunt Guide to Mathematically Rigorous Machine Learning

I recently wrote a brief guide on the Math required for Machine Learning. People liked it, and asked me to write one on how to master ML at a …

Machine Learning

Multiple Linear Regression & Assumptions of Linear Regression: A-Z

Let us a take a break from model building here and understand the first few things which will help us to judge how good is the model which we have …

Algorithms

Machine Learning Results in R: one plot to rule them all! (Part 2 – Regression Models)

Given the number of people interested in my first post for visualizing Classification Models Results, I’ve decided to create and share some new …

Data Science

Why your machine-learning team needs better feature-engineering skills

The skill of feature engineering — crafting data features optimized for machine learning — is as old as data science itself. But it’s a skill I’ve noticed is becoming more and more neglected. The high demand for machine learning has produced a large pool of data scientists who have developed …

Machine Learning

The HTPmod Shiny application enables modeling and visualization of large-scale biological data

1.<p>Chen, D. et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. <i>Plant Cell</i> …

Machine Learning

A Statistical Approach To Timing Entries And Exits In Index Futures.

Assume you are bullish or bearish a certain index or stock, When do you get in? My own approach combines two methods, one is based on deriving …

Bayesian Baby Steps: Normal Next Steps

Enter marquis de Laplace<p>In my first post on Bayesian data analysis, I did a brief overview of how Bayesian updating works using grid approximation to …

Time Series Deep Learning, Part 2: Predicting Sunspot Frequency with Keras LSTM In R

One of the ways <b>Deep Learning can be used in business</b> is to improve the accuracy of time series forecasts (prediction). We recently showed how a Long …

TensorFlow for R

5.1.6 Building the LSTM model<p>Now that we have our data in the required form, let’s finally build the model. As always in deep learning, an important, …

An Intro to Artificial Intelligence

Fundamentals of reinforcement Learning:<p>The key point is giving rewards or points that makes the computer understand that the action they are doing is …

Rate of recovery from perturbations as a means to forecast future stability of living systems

1.<p>Scheffer, M. Critical Transitions in Nature and Society. <i>Princet. Stud. Complex</i>. 384 https://doi.org/10.5860/CHOICE.47-1380 (2009).<p>2.<p>Scheffer, M. <i>et</i> …

Google Scholar

Statistical primer: sample size and power calculations—why, when and how? † | European Journal of Cardio-Thoracic Surgery

When designing a clinical study, a fundamental aspect is the sample size. In this article, we describe the rationale for sample size calculations, …

Statistical Significance Tests for Comparing Machine Learning Algorithms

Comparing machine learning methods and selecting a final model is a common operation in applied machine learning.Models are commonly evaluated using …

NLP In Under 5 Min

AI is the sexiest trend in tech, and NLP (Natural Language Processing) is one of its many branches. NLP is the ability of a machine to understand and …

Choosing the Right Machine Learning Algorithm

Machine learning is part art and part science. When you look at machine learning algorithms, there is no one solution or one approach that fits all. …

Re-introduction to gghighlight: Highlight ggplot2 with Predicates - Wannabe Rstats-fu

Half a year ago, I’ve introduced gghighlight package. I didn’t expect so much R people get interested in my package. Thanks for your attention!<p>But, …

Monte Carlo Part Two

by Jonathan Regenstein<p>In a previous post, we reviewed how to set up and run a Monte Carlo (MC) simulation of future portfolio returns and growth of a …

Anomaly Detection in R

(This article was first published on <b> R-posts.com</b>, and kindly contributed to R-bloggers)The World of AnomaliesImagine you are a credit card selling …

Introduction to Bayesian Networks

<b>Bayesian networks</b> are a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to …

What Is A Confounding Variable

Let’s say a group of researchers, or data scientists discover that the mortality rate in Florida is 20 deaths out of 1000 people a year compared to …

Trustworthy Data Analysis

The success of a data analysis depends critically on the audience. But why? A lot has to do with whether the audience <i>trusts</i> the analysis as well as …

Customer Relationships | Retention Rates

So we have seen the importance of retention rates, and how they affect expected CLV. So the next question is: how do we estimate the retention rates? …

WVPlots now at version 1.0.0 on CRAN!

Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand …

Road Map for Choosing Between Statistical Modeling and Machine Learning | Statistical Thinking

Machine learning (ML) may be distinguished from statistical models (SM) using any of three considerations:<b><br>Uncertainty</b>: SMs explicitly take …

Machine Learning

Machine Learning Crash Course From Google

We’ve been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even …

Plotting Timeseries with map() from purrr

I am trying to generate a plot that is similar to this:A walkthrough is provided here -> …

Predictive policing: Data can be used to prevent crime, but is that data racially tinged?

Predictive policing introduces a scientific element to law enforcement decisions, such as whether to investigate or detain, how long to sentence, and …

Using Box Plots to Explore Women's Height Data

I’ve recently been working on the Digital Panopticon, a digital history project that has brought together (and created) massive amounts of data about …

Seeing Theory

Chapter 1<p>Basic Probability<p>This chapter is an introduction to the basic concepts of probability theory.<p>Chapter 2<p>Compound Probability<p>This chapter …