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Refers to phenomenon where the performance of ML algorithms deteriorates as No. of dimension or feature of input data ⬆️
This is because the volume of space increases exponentially with No. of dimension which causes data to become sparse & distance btwn data point to increase
This is because the volume of space increases exponentially with No. of dimension which causes data to become sparse & distance btwn data point to increase
Many ML algorithms struggle to find meaningful patterns & relationships in high-dimensional data & may suffer from overfitting or poor generalization performance. This can lead to longer training time increased memory requirements & reduced accuracy & efficiency in predictions.
Why is Dimensionality Reduction
necessary?
1⃣Avoids overfitting
2⃣Easier computation
3⃣Improved model performance
4⃣Lower dimensional data requires less storage space.
5⃣Lower dimensional data can work with other algorithms that were unfit for larger dimensions.
necessary?
1⃣Avoids overfitting
2⃣Easier computation
3⃣Improved model performance
4⃣Lower dimensional data requires less storage space.
5⃣Lower dimensional data can work with other algorithms that were unfit for larger dimensions.
To mitigate the curse of dimensionality, techniques such as feature selection, dimensionality reduction & regularization can be used. These methods aim to reduce number of feature or dimensions in the data
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