How do I choose a dimension reduction technique?
An example of an AI chatbot conversation about selecting dimension reduction techniques.
How do I choose a dimension reduction technique for my dataset?
Choosing a dimension reduction technique depends on your data and goals. Are you looking for visualization or to improve model performance?
I want to visualize high-dimensional data. What techniques should I consider?
For visualization, techniques like PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding) are popular. Would you like to know more about one of them?
Yes, can you explain how PCA works?
PCA reduces dimensionality by transforming the data to a new set of variables, the principal components, which capture the most variance.
Are there any limitations to using PCA?
Yes, PCA assumes linear relationships and may not perform well with non-linear data. It's also sensitive to outliers.
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