课程

Dimensionality Reduction in R

基础技能水平

更新时间 2024年12月

Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

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RMachine Learning

4 小时

16 视频

56 练习

4,600 经验值

2,715

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课程描述

Do you ever work with datasets with an overwhelming number of features? Do you need all those features? Which ones are the most important? In this course, you will learn dimensionality reduction techniques that will help you simplify your data and the models that you build with your data while maintaining the information in the original data and good predictive performance.

Why learn dimensionality reduction?

We live in the information age—an era of information overload. The art of extracting essential information from data is a marketable skill. Models train faster on reduced data. In production, smaller models mean faster response time. Perhaps most important, smaller data and models are often easier to understand. Dimensionality reduction is your Occam’s razor in data science.

What will you learn in this course?

The difference between feature selection and feature extraction! Using R, you will learn how to identify and remove features with low or redundant information, keeping the features with the most information. That’s feature selection. You will also learn how to extract combinations of features as condensed components that contain maximal information. That’s feature extraction!

But most importantly, using R’s new tidymodel package, you will use real-world data to build models with fewer features without sacrificing significant performance.

先决条件

Modeling with tidymodels in R

1

Foundations of Dimensionality Reduction

Prepare to simplify large data sets! You will learn about information, how to assess feature importance, and practice identifying low-information features. By the end of the chapter, you will understand the difference between feature selection and feature extraction—the two approaches to dimensionality reduction.

Introduction to dimensionality reduction

50 经验值

Dimensionality and feature information

100 经验值

Mutual information features

100 经验值

Information and feature importance

50 经验值

Calculating root entropy

100 经验值

Calculating child entropies

100 经验值

Calculating information gain of color

100 经验值

The Importance of Dimensionality Reduction in Data and Model Building

50 经验值

Calculate possible combinations

100 经验值

Curse of dimensionality, overfitting, and bias

100 经验值

2

Feature Selection for Feature Importance

Learn how to identify information-rich and information-poor features missing value ratios, variance, and correlation. Then you'll discover how to build tidymodel recipes to select features using these information indicators.

Feature selection vs. feature extraction

50 经验值

Create a zero-variance filter

100 经验值

Create a missing values filter

100 经验值

Feature selection with the combined filter

100 经验值

Selecting based on missing values

50 经验值

Create a missing value ratio filter

100 经验值

Apply a missing value ratio filter

100 经验值

Create a missing values recipe

100 经验值

Selecting based on variance

50 经验值

Create a low-variance filter

100 经验值

Create a low-variance recipe

100 经验值

Selecting based on correlation with other features

50 经验值

Identify highly correlated features

100 经验值

Select correlated feature to remove

50 经验值

Create a high-correlation recipe

100 经验值

3

Feature Selection for Model Performance

Chapter three introduces the difference between unsupervised and supervised feature selection approaches. You'll review how to use tidymodels workflows to build models. Then, you'll perform supervised feature selection using lasso regression and random forest models.

Supervised feature selection

50 经验值

Supervised vs. unsupervised feature selection

100 经验值

Decision tree feature selection type

50 经验值

Model Building and Evaluation with tidymodels

50 经验值

Split out the train and test sets

100 经验值

Create a recipe-model workflow

100 经验值

Fit, explore, and evaluate the model

100 经验值

Lasso Regression

50 经验值

Scale the data for lasso regression

100 经验值

Explore lasso regression penalty values

100 经验值

Tune the penalty hyperparameter

100 经验值

Fit the best model

100 经验值

Random forest models

50 经验值

Create full random forest model

100 经验值

Reduce data using feature importances

100 经验值

Create reduced random forest

100 经验值

4

Feature Extraction and Model Performance

In this final chapter, you'll gain a strong intuition of feature extraction by understanding how principal components extract and combine the most important information from different features. Then learn about and apply three types of feature extraction — principal component analysis (PCA), t-SNE, and UMAP. Discover how you can use these feature extraction methods as a preprocessing step in the tidymodels model-building process.

Foundations of feature extraction - principal components

50 经验值

Understanding principal components

100 经验值

Naming principal components

50 经验值

Principal Component Analysis (PCA)

50 经验值

PCA: variance explained

50 经验值

Mapping features to principal components

100 经验值

PCA in tidymodels

100 经验值

t-Distributed Stochastic Neighborhood Embedding (t-SNE)

50 经验值

Separating house prices with PCA

100 经验值

Separating house prices with t-SNE

100 经验值

Uniform Manifold Approximation and Projection (UMAP)

50 经验值

Separating house prices with UMAP

100 经验值

UMAP reduction in a decision tree model

100 经验值

Evaluate the UMAP decision tree model

100 经验值

50 经验值

Dimensionality Reduction in R

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