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Diffstat (limited to 'SI/Resource/Data Science/Machine Learning/Contents')
3 files changed, 3 insertions, 6 deletions
diff --git a/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md b/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md index 938bf62..1a59925 100644 --- a/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md +++ b/SI/Resource/Data Science/Machine Learning/Contents/Bias and Variance.md @@ -7,7 +7,6 @@ tags: - Machine-Learning - Bias-and-Variance --- - # Bias ## Training Data (80~90%) vs. Test Data (10~20%) @@ -42,4 +41,4 @@ tags: 1. train data $\uparrow$ 2. bias error $\downarrow$ and variance error $\uparrow$ 3. cost $\uparrow$ - - [[Regularization]] loss function
\ No newline at end of file + - [[Regularization]] loss function 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]] diff --git a/SI/Resource/Data Science/Machine Learning/Contents/Gradient descent.md b/SI/Resource/Data Science/Machine Learning/Contents/Gradient descent.md index fdf8905..6d047a1 100644 --- a/SI/Resource/Data Science/Machine Learning/Contents/Gradient descent.md +++ b/SI/Resource/Data Science/Machine Learning/Contents/Gradient descent.md @@ -7,7 +7,6 @@ tags: - Machine-Learning - Gradient-descent --- - # Gradient Descent - Update parameters that minimize values of loss functions @@ -18,4 +17,4 @@ tags: 1. Find the derivative of the loss function at the current parameters. 2. Update parameters in the opposite direction of the derivative -3. Repeat steps 1 and 2 as many epochs (hyperparameter) until the differential value becomes 0.
\ No newline at end of file +3. Repeat steps 1 and 2 as many epochs (hyperparameter) until the differential value becomes 0. |