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Feature Engineering and Dimensionality Reduction

12 resources created by 11 users

Feature Engineering and Dimensionality Reduction

Top Feature Engineering and Dimensionality Reduction resources

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Principal Component Analysis Why learn PCA? The advantage of using PCA is: If you want to visualize the high dimensional data, then our pair plot, scatter plot all of them will not work. So, we always convert the data into 2 or 3 dimensions and t... Rohit Gurjar
Dimensionality reduction and Visualization: What is Dimensionality reduction? We realize that we can visualize data in 2-D or 3-D using a simple scatter plot and for 4-D,5-D and even 6-D, we use pair plot to sense how data is. Note: If we have to visualize our ... Rohit Gurjar
Clustering & Dimensionality Reduction Clustering Unsupervised Learning We don't have actual labels We may have groups, but nothing is defined and there are no correct answers Why use unsupervised Learning? Find similar data groups (clusters) Find outliers... Laura BURGOS
PCA - created from Mind Map Step 1: Covariance /Covariance Matrix of the dataset Motivation for PCA Step 0: Standarize dataset of dim(d) Priscilla Peng

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