David Kofoed Wind posted an article to the Kaggle blog No Free Hunch titled “Learning from the best“. In the post, David summarized 6 key areas related to participating and doing well in competitive machine learning with quotes from top performing kagglers. In this post you will discover the key heuristics for doing well in […]

## How To Get Better At Machine Learning

Colorado Reed from Metacademy wrote a great post recently titled “Level-Up Your Machine Learning” to answer the question he often receives of: What should I do if I want to get ‘better’ at machine learning, but I don’t know what I want to learn? In this post you will discover a summary of Colorado recommendations […]

## Non-Linear Classification in R

In this post you will discover 8 recipes for non-linear classification in R. Each recipe is ready for you to copy and paste and modify for your own problem. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of […]

## Linear Classification in R

In this post you will discover recipes for 3 linear classification algorithms in R. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of each observation to one of three flower species. Let’s get started. Logistic […]

## Clever Application Of A Predictive Model

What if you could use a predictive model to find new combinations of attributes that do not exist in the data but could be valuable. In Chapter 10 of Applied Predictive Modeling, Kuhn and Johnson provide a case study that does just this. It’s a fascinating and creative example of how to use a predictive […]

## Improve Model Accuracy with Data Pre-Processing

Data preparation can make or break the predictive ability of your model. In Chapter 3 of their book Applied Predictive Modeling, Kuhn and Johnson introduce the process of data preparation. They refer to it as the addition, deletion or transformation of training set data. In this post you will discover the data pre-process steps that […]

## Model Prediction Accuracy Versus Interpretation in Machine Learning

In their book Applied Predictive Modeling, Kuhn and Johnson comment early on the trade-off of model prediction accuracy versus model interpretation. For a given problem, it is critical to have a clear idea of the which is a priority, accuracy or explainability so that this trade-off can be made explicitly rather than implicitly. In this […]

## Non-Linear Regression in R with Decision Trees

In this post, you will discover 8 recipes for non-linear regression with decision trees in R. Each example in this post uses the longley dataset provided in the datasets package that comes with R. The longley dataset describes 7 economic variables observed from 1947 to 1962 used to predict the number of people employed yearly. Let’s get started. Classification […]

## Non-Linear Regression in R

In this post you will discover 4 recipes for non-linear regression in R. There are many advanced methods you can use for non-linear regression, and these recipes are but a sample of the methods you could use. Let’s get started. Each example in this post uses the longley dataset provided in the datasets package that comes with R. The […]

## Penalized Regression in R

In this post you will discover 3 recipes for penalized regression for the R platform. You can copy and paste the recipes in this post to make a jump-start on your own problem or to learn and practice with linear regression in R. Let’s get started. Each example in this post uses the longley dataset provided in the datasets […]