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Dimensionality Reduction in Python

t-SNE visualization of high-dimensional data

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Este exercício faz parte do curso

Dimensionality Reduction in Python

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You'll be introduced to the concept of dimensionality reduction and will learn when an why this is important. You'll learn the difference between feature selection and feature extraction and will apply both techniques for data exploration. The chapter ends with a lesson on t-SNE, a powerful feature extraction technique that will allow you to visualize a high-dimensional dataset.

Exercise 1: Introduction Exercise 2: Finding the number of dimensions in a dataset Exercise 3: Removing features without variance Exercise 4: Feature selection vs. feature extraction Exercise 5: Visually detecting redundant features Exercise 6: Advantage of feature selection Exercise 7: t-SNE visualization of high-dimensional data

Exercício atual

Exercise 8: t-SNE intuition Exercise 9: Fitting t-SNE to the ANSUR data Exercise 10: t-SNE visualisation of dimensionality

In this first out of two chapters on feature selection, you'll learn about the curse of dimensionality and how dimensionality reduction can help you overcome it. You'll be introduced to a number of techniques to detect and remove features that bring little added value to the dataset. Either because they have little variance, too many missing values, or because they are strongly correlated to other features.

Exercise 1: The curse of dimensionality Exercise 2: Train - test split Exercise 3: Fitting and testing the model Exercise 4: Accuracy after dimensionality reduction Exercise 5: Features with missing values or little variance Exercise 6: Finding a good variance threshold Exercise 7: Features with low variance Exercise 8: Removing features with many missing values Exercise 9: Pairwise correlation Exercise 10: Correlation intuition Exercise 11: Inspecting the correlation matrix Exercise 12: Visualizing the correlation matrix Exercise 13: Removing highly correlated features Exercise 14: Filtering out highly correlated features Exercise 15: Nuclear energy and pool drownings

In this second chapter on feature selection, you'll learn how to let models help you find the most important features in a dataset for predicting a particular target feature. In the final lesson of this chapter, you'll combine the advice of multiple, different, models to decide on which features are worth keeping.

Exercise 1: Selecting features for model performance Exercise 2: Building a diabetes classifier Exercise 3: Manual Recursive Feature Elimination Exercise 4: Automatic Recursive Feature Elimination Exercise 5: Tree-based feature selection Exercise 6: Building a random forest model Exercise 7: Random forest for feature selection Exercise 8: Recursive Feature Elimination with random forests Exercise 9: Regularized linear regression Exercise 10: Creating a LASSO regressor Exercise 11: Lasso model results Exercise 12: Adjusting the regularization strength Exercise 13: Combining feature selectors Exercise 14: Creating a LassoCV regressor Exercise 15: Ensemble models for extra votes Exercise 16: Combining 3 feature selectors

This chapter is a deep-dive on the most frequently used dimensionality reduction algorithm, Principal Component Analysis (PCA). You'll build intuition on how and why this algorithm is so powerful and will apply it both for data exploration and data pre-processing in a modeling pipeline. You'll end with a cool image compression use case.

Exercise 1: Feature extraction Exercise 2: Manual feature extraction I Exercise 3: Manual feature extraction II Exercise 4: Principal component intuition Exercise 5: Principal component analysis Exercise 6: Calculating Principal Components Exercise 7: PCA on a larger dataset Exercise 8: PCA explained variance Exercise 9: PCA applications Exercise 10: Understanding the components Exercise 11: PCA for feature exploration Exercise 12: PCA in a model pipeline Exercise 13: Principal Component selection Exercise 14: Selecting the proportion of variance to keep Exercise 15: Choosing the number of components Exercise 16: PCA for image compression Exercise 17: Congratulations!