---
# System prepended metadata

title: text result

---

# text result

## tf-idf 4000 word
### Category:  openness 

[[0 4 0]
 [0 5 0]
 [0 6 0]]
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         4
           1       0.33      1.00      0.50         5
           2       0.00      0.00      0.00         6

    accuracy                           0.33        15
   macro avg       0.11      0.33      0.17        15
weighted avg       0.11      0.33      0.17        15

0.3333333333333333
**average k-fold accuracy:** 0.3004945054945055 

### Category:  outdoor 

[[1 5]
 [4 5]]
              precision    recall  f1-score   support

           0       0.20      0.17      0.18         6
           1       0.50      0.56      0.53         9

    accuracy                           0.40        15
   macro avg       0.35      0.36      0.35        15
weighted avg       0.38      0.40      0.39        15

0.4
**average k-fold accuracy:** 0.5061904761904763 

## Chi-square 500
### Category:  openness 

[[0 3 1]
 [0 4 1]
 [1 5 0]]
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         4
           1       0.33      0.80      0.47         5
           2       0.00      0.00      0.00         6

    accuracy                           0.27        15
   macro avg       0.11      0.27      0.16        15
weighted avg       0.11      0.27      0.16        15

0.26666666666666666
0.3478296703296703 

### Category:  outdoor 

[[4 2]
 [0 9]]
              precision    recall  f1-score   support

           0       1.00      0.67      0.80         6
           1       0.82      1.00      0.90         9

    accuracy                           0.87        15
   macro avg       0.91      0.83      0.85        15
weighted avg       0.89      0.87      0.86        15

0.8666666666666667
0.7642857142857142 

## chi-square 50

### Category:  openness 

[[0 2 2]
 [0 3 2]
 [1 2 3]]
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         4
           1       0.43      0.60      0.50         5
           2       0.43      0.50      0.46         6

    accuracy                           0.40        15
   macro avg       0.29      0.37      0.32        15
weighted avg       0.31      0.40      0.35        15

0.4
0.39645604395604395 

### Category:  outdoor 

[[4 2]
 [1 8]]
              precision    recall  f1-score   support

           0       0.80      0.67      0.73         6
           1       0.80      0.89      0.84         9

    accuracy                           0.80        15
   macro avg       0.80      0.78      0.78        15
weighted avg       0.80      0.80      0.80        15

0.8
0.7104761904761905

# Summary:
tf-idf 4000:
1. openness
    * Sample accuracy: 0.3333333333333333
    * average k-fold accuracy: 0.3004945054945055
2. outdoor
    * Sample accuracy: 0.4
    * average k-fold accuracy: 0.5061904761904763

Chi-square 500:
1. openness
    * Sample accuracy: 0.26666666666666666
    * average k-fold accuracy: 0.3478296703296703
2. outdoor
    * Sample accuracy: 0.8666666666666667
    * average k-fold accuracy: 0.7642857142857142

Chi-square 50:
1. openness
    * Sample accuracy: 0.4
    * average k-fold accuracy: 0.39645604395604395
2. outdoor
    * Sample accuracy: 0.8
    * average k-fold accuracy: 0.7104761904761905

count Chi-square 500:
1. openness
    * Sample accuracy: 0.3
    * average k-fold accuracy: 0.3166666666666666 
2. outdoor
    * Sample accuracy: 0.5
    * average k-fold accuracy: 0.576310715609937 

count Chi-square 50:
1. openness
    * Sample accuracy: 0.4
    * average k-fold accuracy: 0.33689655172413796 
2. outdoor
    * Sample accuracy: 0.43333333333333335
    * average k-fold accuracy: 0.5252836484983314 


# Model difference(tf-idf chi 500)

## Personality
* Openess:
    * random forest = 0.3466666666666667 
    * svm = 0.3704597701149425 
    * xgboost = 0.33999999999999997 

* conscientiousness:
    * random forest = 0.2949430584247047  
    * svm = 0.3704597701149425 
    * xgboost = 0.2691686530006886 

* extraversion:
    * random forest = 0.3579569892473119 
    * svm = 0.3704597701149425 
    * xgboost = 0.369715821812596 

* aggreablness:
    * random forest = 0.298963133640553 
    * svm = 0.3704597701149425 
    * xgboost = 0.30595238095238086 

* neuroticism:
    * random forest = 0.40956989247311826
    * svm = 0.3704597701149425 
    * xgboost = 0.42496159754224266 


* OutDoor:
    * random forest = 0.711505376344086 
    * svm = 0.5251761216166111 
    * xgboost = 0.5456136447905079

# picture result

### Category:  openness 
1. xgboost : 0.39452838827838826
2. random tree : 0.4666666666666667 
3. svm : 0.3719322344322344 

### Category:  conscientiousness 

1. xgboost : 0.3487271062271063
2. random tree :0.3246199633699634 
3. svm : 0.26666666666666666  

### Category:  extraversion 

1. xgboost :0.35042582417582413
2. random tree :0.36567765567765564 
3. svm : 0.39228021978021976 

### Category:  agreeableness 

1. xgboost :0.41208333333333336 
2. random tree : 0.35720238095238094  
3. svm : 0.3710714285714286 

### Category:  neuroticism 

1. xgboost :0.3020604395604395
2. random tree :0.33744505494505495 
3. svm : 0.39228021978021976 


### Category:  outdoor 

[[1 5]
 [4 5]]
              precision    recall  f1-score   support

           0       0.20      0.17      0.18         6
           1       0.50      0.56      0.53         9

    accuracy                           0.40        15
   macro avg       0.35      0.36      0.35        15
weighted avg       0.38      0.40      0.39        15

0.4
**average k-fold accuracy:** 0.5061904761904763 

## Chi-square 500
### Category:  openness 

[[0 3 1]
 [0 4 1]
 [1 5 0]]
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         4
           1       0.33      0.80      0.47         5
           2       0.00      0.00      0.00         6

    accuracy                           0.27        15
   macro avg       0.11      0.27      0.16        15
weighted avg       0.11      0.27      0.16        15

0.26666666666666666
0.3478296703296703 

### Category:  outdoor 

[[4 2]
 [0 9]]
              precision    recall  f1-score   support

           0       1.00      0.67      0.80         6
           1       0.82      1.00      0.90         9

    accuracy                           0.87        15
   macro avg       0.91      0.83      0.85        15
weighted avg       0.89      0.87      0.86        15

0.8666666666666667
0.7642857142857142 

## chi-square 50

### Category:  openness 

[[0 2 2]
 [0 3 2]
 [1 2 3]]
              precision    recall  f1-score   support

           0       0.00      0.00      0.00         4
           1       0.43      0.60      0.50         5
           2       0.43      0.50      0.46         6

    accuracy                           0.40        15
   macro avg       0.29      0.37      0.32        15
weighted avg       0.31      0.40      0.35        15

0.4
0.39645604395604395 

### Category:  outdoor 

[[4 2]
 [1 8]]
              precision    recall  f1-score   support

           0       0.80      0.67      0.73         6
           1       0.80      0.89      0.84         9

    accuracy                           0.80        15
   macro avg       0.80      0.78      0.78        15
weighted avg       0.80      0.80      0.80        15

0.8
0.7104761904761905

# Summary:
tf-idf 4000:
1. openness
    * Sample accuracy: 0.3333333333333333
    * average k-fold accuracy: 0.3004945054945055
2. outdoor
    * Sample accuracy: 0.4
    * average k-fold accuracy: 0.5061904761904763

Chi-square 500:
1. openness
    * Sample accuracy: 0.26666666666666666
    * average k-fold accuracy: 0.3478296703296703
2. outdoor
    * Sample accuracy: 0.8666666666666667
    * average k-fold accuracy: 0.7642857142857142

Chi-square 50:
1. openness
    * Sample accuracy: 0.4
    * average k-fold accuracy: 0.39645604395604395
2. outdoor
    * Sample accuracy: 0.8
    * average k-fold accuracy: 0.7104761904761905

count Chi-square 500:
1. openness
    * Sample accuracy: 0.3
    * average k-fold accuracy: 0.3166666666666666 
2. outdoor
    * Sample accuracy: 0.5
    * average k-fold accuracy: 0.576310715609937 

count Chi-square 50:
1. openness
    * Sample accuracy: 0.4
    * average k-fold accuracy: 0.33689655172413796 
2. outdoor
    * Sample accuracy: 0.43333333333333335
    * average k-fold accuracy: 0.5252836484983314 


# Model difference(tf-idf chi 500)

## Personality
* Openess:
    * random forest = 0.3466666666666667 
    * svm = 0.3704597701149425 
    * xgboost = 0.33999999999999997 

* conscientiousness:
    * random forest = 0.2949430584247047  
    * svm = 0.3704597701149425 
    * xgboost = 0.2691686530006886 

* extraversion:
    * random forest = 0.3579569892473119 
    * svm = 0.3704597701149425 
    * xgboost = 0.369715821812596 

* aggreablness:
    * random forest = 0.298963133640553 
    * svm = 0.3704597701149425 
    * xgboost = 0.30595238095238086 

* neuroticism:
    * random forest = 0.40956989247311826
    * svm = 0.3704597701149425 
    * xgboost = 0.42496159754224266 


* OutDoor:
    * random forest = 0.711505376344086 
    * svm = 0.5251761216166111 
    * xgboost = 0.5456136447905079


把少的一邊複製，加重權重 smote 
class weight
scale_pos_weight