Update README.md
Browse files
README.md
CHANGED
|
@@ -90,9 +90,9 @@ while negative examples are those that do not belong to that label.
|
|
| 90 |
For example, positive examples for `joy` are all the inputs with `joy` as target, while negative examples are inputs from sadness, fear, love, anger, and surprise.
|
| 91 |
|
| 92 |
`False Positive Rate` heatmap shows the proportion of negative samples that are incorrectly classified as positive.
|
| 93 |
-
The `joy` class with the length between 60 and 75 is the worst (0.5) because we only have two test examples related to this group.
|
| 94 |
One of the examples is classified wrongly as `joy`. The outcome makes sense because the training data with
|
| 95 |
-
sequence
|
| 96 |
|
| 97 |
`False Negative Rate` heatmap displays the proportion of positive samples that are incorrectly classified as negative.
|
| 98 |
You should notice darker colors (0.56) in the cells of the `surprise` class, especially for sequence length ranges 0 - 15 and 30 - 45.
|
|
@@ -102,8 +102,8 @@ For the `sadness` class with sequence length (60 - 75), the situation is the sam
|
|
| 102 |
|
| 103 |
`False Discovery Rate` heatmap depicts the proportion of positive predictions that are incorrectly classified.
|
| 104 |
The darker cells (0.25) belong to the `surprise` class with sequence length ranges (0 - 15) and (45 - 60).
|
| 105 |
-
The other two cells of `surprise` class are better with 0.16 and 0 respectively. The same goes for the `fear` class with the darkest cell for (45 - 60) range group compared to other ranges.
|
| 106 |
-
This could be explained by the number of training examples available for the concerned sequence length ranges.
|
| 107 |
For `surprise` class, most training examples are (15 - 30) in terms of sequence length.
|
| 108 |
For `fear` class, most examples have sequence lengths of (0 - 15) and (15 - 30).
|
| 109 |
Limited training examples from certain groups could produce poor results when the model is evaluated with the data from these groups.
|
|
|
|
| 90 |
For example, positive examples for `joy` are all the inputs with `joy` as target, while negative examples are inputs from sadness, fear, love, anger, and surprise.
|
| 91 |
|
| 92 |
`False Positive Rate` heatmap shows the proportion of negative samples that are incorrectly classified as positive.
|
| 93 |
+
The `joy` class with the length group between 60 and 75 is the worst (0.5) because we only have two test examples related to this group.
|
| 94 |
One of the examples is classified wrongly as `joy`. The outcome makes sense because the training data with
|
| 95 |
+
sequence length between 60 and 75 are the least. This explains why the model performs worst in this length group.
|
| 96 |
|
| 97 |
`False Negative Rate` heatmap displays the proportion of positive samples that are incorrectly classified as negative.
|
| 98 |
You should notice darker colors (0.56) in the cells of the `surprise` class, especially for sequence length ranges 0 - 15 and 30 - 45.
|
|
|
|
| 102 |
|
| 103 |
`False Discovery Rate` heatmap depicts the proportion of positive predictions that are incorrectly classified.
|
| 104 |
The darker cells (0.25) belong to the `surprise` class with sequence length ranges (0 - 15) and (45 - 60).
|
| 105 |
+
The other two cells of `surprise` class are better with 0.16 and 0 respectively. The same goes for the `fear` class with the darkest cell (0.22) for (45 - 60) range group compared to other ranges.
|
| 106 |
+
This situation could be explained by the number of training examples available for the concerned sequence length ranges.
|
| 107 |
For `surprise` class, most training examples are (15 - 30) in terms of sequence length.
|
| 108 |
For `fear` class, most examples have sequence lengths of (0 - 15) and (15 - 30).
|
| 109 |
Limited training examples from certain groups could produce poor results when the model is evaluated with the data from these groups.
|