Machine Learning

Penalized Regression in R

Last Updated on August 15, 2020 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. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples. Let’s get started. Penalized RegressionPhoto by Bay Area […]

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Machine Learning

Non-Linear Regression in R

Last Updated on August 15, 2020 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. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples. Let’s get started. Non-Linear RegressionPhoto by Steve Jurvetson, some rights reserved Each example […]

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Machine Learning

Non-Linear Regression in R with Decision Trees

Last Updated on August 15, 2020 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. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all […]

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Machine Learning

Model Prediction Accuracy Versus Interpretation in Machine Learning

Last Updated on August 15, 2020 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 post you will discover and consider this important trade-off. Model Accuracy vs ExplainabilityPhoto by Donald Hobern, some rights reserved […]

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Machine Learning

Improve Model Accuracy with Data Pre-Processing

Last Updated on August 15, 2020 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 you can use to improve the predictive ability of your models. Kick-start your project with my new book Data […]

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Machine Learning

Clever Application Of A Predictive Model

Last Updated on August 15, 2020 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 model. In this post we will discover this less obvious use of a predictive model and the types of […]

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Machine Learning

Linear Classification in R

Last Updated on August 22, 2019 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. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all […]

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Machine Learning

Non-Linear Classification in R

Last Updated on August 22, 2019 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 each observation to one of three flower species. Kick-start your project with my new book Machine Learning Mastery With […]

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Machine Learning

How To Get Better At Machine Learning

Last Updated on August 15, 2020 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 and a breakdown of his roadmap. Level-up Your Machine LearningPhoto by Helgi Halldórsson, some rights reserved Strategy To Do […]

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Machine Learning

How to Kick Ass in Competitive Machine Learning

Last Updated on June 7, 2016 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 competitive machine learning distilled from that post. Learning from the bestPhoto by Lida, some rights reserved Learning from Kaggle […]

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