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| author | TheSiahxyz <164138827+TheSiahxyz@users.noreply.github.com> | 2024-07-29 14:14:27 +0900 |
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| committer | TheSiahxyz <164138827+TheSiahxyz@users.noreply.github.com> | 2024-07-29 14:14:27 +0900 |
| commit | 2f32142ea7162aa05ac33e4e89df5b9a1c657ddd (patch) | |
| tree | 6bf8bec6680b7d8c819e9a88bf04f8c7756fd440 /SI/Resource/Data Science/Machine Learning/Contents/Classification.md | |
| parent | 8abb4730aff5f380a2e0f3a29b5fe439817d28a0 (diff) | |
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Diffstat (limited to 'SI/Resource/Data Science/Machine Learning/Contents/Classification.md')
| -rw-r--r-- | SI/Resource/Data Science/Machine Learning/Contents/Classification.md | 3 |
1 files changed, 1 insertions, 2 deletions
diff --git a/SI/Resource/Data Science/Machine Learning/Contents/Classification.md b/SI/Resource/Data Science/Machine Learning/Contents/Classification.md index 12c125e..d3b908d 100644 --- a/SI/Resource/Data Science/Machine Learning/Contents/Classification.md +++ b/SI/Resource/Data Science/Machine Learning/Contents/Classification.md @@ -7,11 +7,10 @@ tags: - Machine-Learning - Classification --- - # Classification Classification in the context of machine learning and statistics is a type of supervised learning approach where the output variable is a category, such as "spam" or "not spam", or "disease" and "no disease". In classification, an algorithm is trained on a dataset of labeled examples, learning to associate input data points with the corresponding category label. Once trained, the model can then categorize new, unseen data points. 1. Input: Continuous (float), Discrete (categorical), etc. 2. Output: Discrete (categorical) -3. Model types: Binary - [[Sigmoid]], polynomial - [[softmax]]
\ No newline at end of file +3. Model types: Binary - [[Sigmoid]], polynomial - [[softmax]] |