- Formula: @@0@@

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**Precision scores**:`tp/(tp+fp)`

. Intuitively, the precision scores tell out of all the correct prediction what percentage is the classifier correct true class and ground truth is true**Recall scores**:`tp/(tp+fn)`

. Intuitively, the recall scores tell the ability of classifier to find positve samples out of the data space**F1 scores**: weighted average of the precision and recall @@0@@

- True Positive(TP): predicted yes and ground truth = yes
- True Negative(TN): predicted no and ground truth = no
- False Positive(FP): predicted yes and ground truth = no
- False Negative(FN): predicted no and ground truth = yes

- NOTE: we try to match our implementation with sk_learn therefore, the order of the confusion matrix is as follow

```
1
2
3
4
```

```
Predicted 0 1
True
0 tn fp
1 fn tp
```

`1 2`

`np.array([[tn, fp], [fn, tp]])`

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- The receive operating characteristic (ROC) is a diagnotics tool as it probability thresh varies. This is a tool to select the best probability threshold for model when create a binary classification model.
- created by plotting the TPR and FPR at various thresh hold setting.
- Formula link
True positive rate (TPR) @@0@@

False positive rate (FPR): @@1@@

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- Use
`def = df.sample(frac=1)`

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- After the comparison above, standardize data is used for the remainder of the data as it gives a better performance.

After the comparison above, distance weightage is preferred as it gives a better performance.

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