Model Performance

1 Why do model evaluation?

• 这个部分相当于是你做完clean/feature engineering/ model，就到model evaluation了，说白了就是说，你模型到底如何

• 对应How can we compare this model with others model?

2 How to evaluate a model?

What data to evaluate

• select and split data

What method to use（实验评估方法/Model Selection）

Cross Validation

• What is Cross Validation?

  • Assess how your model result will generalize to another independent data set.

  • Predict and test on the same data is a methodological mistake

  • There are several cross validation techniques, popular is k-fold cross validation

K-fold Cross Validation

What metrics to compare（性能度量performance measure）

Classification-Confusion matrix

• picture
• TP/FN/FP/TN；True/False；Postive/Negative

  • TP: true positive（真正例）; FN: false negative（假反例）(type II)

  • FP: false positive（假正例type I）; TN: true negative（真反例）

  • True/False means if you made a correct/wrong prediction

  • Positive/Negative means what your prediction is/is not

• Accuracy

  • $\frac{TP+TN}{TP+TN+FP+FN}$​​​​​​​​​​​​​​​​​​​​​​​​​​

  • 预测中你对的多少个

• Precision

  • $P=\frac{TP}{TP+FP}$​​​​​​​​​​​​​​​​​​​

  • 查准率precision：你说的准确到底有多少可以相信的

  • 在所有你认为positive的数据中，有多少真的是positive？

  • example, spam email：要求precision高（杀的必须准）

• Recall Sensitivity

  • $R=\frac{TP}{TP+FN}$

  • （查全率recall：真的是不是都预测对了）查全率recall：真的是不是都预测对了

  • 在所有positive的数据中，有多少被你正确地识别出来（是positive）

  • disease/cybersecuirity：要求recall高( 宁可错杀)

  • 如果negative很重要，你看recall，反之你看precision更关注positive

• F1

  • $F1=\frac{2TP}{2TP+FP+FN}=\frac{2}{\frac{1}{recall}+\frac{1}{precision}}$

  • (可以统一recall&precision)

  • 越高越好

Classification-ROC

• Receiver operation characteristic curve（根据你的threshold来制定的，threshold是用来就判断是positive or negative，像logistic regression里，我高于0.8,判定为positive，当然你可以选0.6）

• Define False Positive Rate as X axis, True Positive Rate as Y axis

• The receiver operating curve, also noted ROC, is the plot of TPR versus FPR by varying the threshold.

• Special Points in ROC space

  • best case(0,1); worst case:(1,0)

  • 对角线上的点：

    • 当threshold设定为最高时，所有样本都被预测为negative，此时得到的点在(0,0).

    • 当threshold设定为最低时，所有样本都被预测为positive，此时得到的点在(1,1)

  • why does Equal Error rate mean FPR = FNR?

    • 已知固定关系： FNR=1-TP/Number of real positive = 1- TPR

    • 根据图中焦点性质可知： FPR （x）= 1-TPR（y）

    • FPR=FNR

Classification-AUC

• 你想完整的表示前面的auc么？

  • Area under the curve of ROC(AUC)

  • AUC value: [0,1]

  • The larger the value is, the better classification performance your classifier has.

  • AUC value is a probability value.

• 面试题：机器学习里0-1的值，都希望有一个概率。怎么用概率来解释AUC？

  • ROC AUC is the probability that a randomly-chosen positive example is ranked more highly than a randomly-chosen negative example.

Regression

• ERM，empirical risk measure：

• coefficient of determination

3 Failures Analysis

• 不同的策略来解决减少failures（把你的failures的分类）

  • 可以通过模型解决

  • 可以通过调参解决

  • 可以通过data解决(可能你少一部分data)

  • 不能解决

• failures analysis的目的就是进一步提高模型的性能，进行迭代开发，retraining

• Summary

  • cross validation——找骨架（model selection/model infrastructure）

  • mixed validation and training data into training——找肉，例如y=ax+b里面的a和b

  • model evaluation——ROC，AUC，Precision，Recall

  • failure analysis——确定问题在哪里，然后更新step1-3

Machine Learning End-to-End Pipeline

Business Design

Data acquisition

• collect data from outside system

• pre-process into the format that needed

Data preparation

• Clean, transform, validate and select data

• annotation

• feature extraction

• prepare data into: training, validation and evaluation datasets

Training & Validation

• Select the model

• Train the model

• Tune the model

• Validate the training process

Testing Evaluation

• evaluate the performance metrics on the evaluation dataset

• analyze failures

Deployment& Inference

• Integrated into production apps

• config models based on system needs

• predict on real-life data

• log failures/errors