Machines Learning 学习笔记(Week6)
4434 단어 MachineLearning
Evaluating a Hypothesis
1. Model Selection
Break down our dataset into three sets:
- Traning set: 60%
- Cross Validation set: 20%
- Test set: 20%
Suppose we have several hypothesis functions with different polynomial degrees. To select the best model:
2. Test Set Error
J_{test}(Θ) = \frac{1}{2m_{test}} \sum_{i=1}^{m_{test}} \bigl(h_Θ(x_{test}^{(i)})-y_{test}^{(i)} \bigr)^2
err\bigl(h_Θ(x),y\bigr) =
\begin{array}{ll}
1 & if \, h_Θ(x)\geq0.5 \, and \, y=0 \, or \, h_Θ(x)\leq0.5 \, and \, y=1 \\
0 & otherwise
\end{array}
The average test error for the test set is:
Test Error = $\frac {1}{m_test}\sum_{i=1}^{m_{test}}err\bigl(h_Θ(x_{test}^{(i)}),y_{test}^{ (i)}\bigr)$
This gives us the proportion of the test data that was misclassified.
Bias vs. Variance
1. Degree of the Polynomial d and B/V
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2. Regularization and B/V
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How to choose $\lambda$ :
3. Learning Curves
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4. What to Do Next to Improve
Our decision process can be broken down as follows:
Getting more training examples: Fixes high variance
Trying smaller sets of features: Fixes high variance
Adding features: Fixes high bias
Adding polynomial features: Fixes high bias
Decreasing λ: Fixes high bias
Increasing λ: Fixes high variance
오류 분석
Choose Error Metrics:
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*Precision, Recall and F1 Score are good metrics paricularly when dealing with skewed data.
Using large data sets usually helps!
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It’s not who has the best algorithm that wins.
It’s who has the most data.
Reference
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