Understanding "Selection Variable"
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📊Dimensionality reduction in PCA (Principal Component Analysis) means reducing the number of variables (features) in your dataset while keeping as much important information (variance) as possible.📊 🌿 1. What does "dimensionality" mean? In data, each variable or feature is a dimension. Example: If you measure plant height, leaf number, and chlorophyll content, your data has 3 dimensions. ⚙️ 2. What is dimensionality reduction? It’s the process of converting a high-dimensional dataset (many va
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📊Dimensionality reduction in PCA (Principal Component Analysis) means reducing the number of variables (features) in your dataset while keeping as much important information (variance) as possible.📊 🌿 1. What does "dimensionality" mean? In data, each variable or feature is a dimension. Example: If you measure plant height, leaf number, and chlorophyll content, your data has 3 dimensions. ⚙️ 2. What is dimensionality reduction? It’s the process of converting a high-dimensional dataset (many va
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