Interactive Course

Machine Learning Toolbox

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

4 hours
24 Videos
88 Exercises
32,767 Participants
6,200 XP

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Course Description

Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular caret R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.

In the first chapter of this course, you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).

In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.

In the final chapter of this course, you'll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).

In this chapter, you'll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).

In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.

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Regression models: fitting them and evaluating their performance
Free

In the first chapter of this course, you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
View Chapter Details Play Chapter Now
Classification models: fitting them and evaluating their performance

In this chapter, you'll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
View Chapter Details Play Chapter Now
Tuning model parameters to improve performance

In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.
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Preprocessing your data

In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.
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Selecting models: a case study in churn prediction

In the final chapter of this course, you'll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).
View Chapter Details Play Chapter Now

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Zachary Deane-Mayer

Automation First Data Scientist at DataRobot

Zach is a Data Scientist at DataRobot and co-author of the caret R package. He's fascinated by predicting the future and spends his free time competing in predictive modeling competitions. He's currently one of top 500 data scientists on Kaggle and took 9th place in the Heritage Health Prize as part of the Analytics Inside team.

Max Kuhn

Software Engineer at RStudio and creator of caret

Dr. Max Kuhn is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, AppliedPredictiveModeling, Cubist, C50 and SparseLDA. He routinely teaches classes in predictive modeling at Predictive Analytics World and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology.

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Machine Learning Toolbox

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Course Description

Regression models: fitting them and evaluating their performance

Welcome to the Toolbox

In-sample RMSE for linear regression

In-sample RMSE for linear regression on diamonds

Out-of-sample error measures

Out-of-sample RMSE for linear regression

Randomly order the data frame

Try an 80/20 split

Predict on test set

Calculate test set RMSE by hand

Comparing out-of-sample RMSE to in-sample RMSE

Cross-validation

Advantage of cross-validation

10-fold cross-validation

5-fold cross-validation

5 x 5-fold cross-validation

Making predictions on new data

Tuning model parameters to improve performance

Random forests and wine

Random forests vs. linear models

Fit a random forest

Explore a wider model space

Advantage of a longer tune length

Try a longer tune length

Custom tuning grids

Advantages of a custom tuning grid

Fit a random forest with custom tuning

Introducing glmnet

Advantage of glmnet

Make a custom trainControl

Fit glmnet with custom trainControl

glmnet with custom tuning grid

Why a custom tuning grid?

glmnet with custom trainControl and tuning

Interpreting glmnet plots

Selecting models: a case study in churn prediction

Reusing a trainControl

Why reuse a trainControl?

Make custom train/test indices

Reintroducing glmnet

glmnet as a baseline model

Fit the baseline model

Reintroducing random forest

Random forest drawback

Random forest with custom trainControl

Comparing models

Matching train/test indices

Create a resamples object

More on resamples

Create a box-and-whisker plot

Create a scatterplot

Ensembling models

Summary

Classification models: fitting them and evaluating their performance

Logistic regression on sonar

Why a train/test split?

Try a 60/40 split

Fit a logistic regression model

Confusion matrix

Confusion matrix takeaways

Calculate a confusion matrix

Calculating accuracy

Calculating true positive rate

Calculating true negative rate

Class probabilities and predictions

Probabilities and classes

Try another threshold

From probabilites to confusion matrix

Introducing the ROC curve