### Confusion matrix
Per default, Nkululeko displays results as a confusion matrix using binning with regression.

![Confusion matrix](./images/conf_mat.png)

### Epoch progression
The point when overfitting starts can sometimes be seen by looking at the results per epoch:

![Epoch progression](./images/epoch_progression.png)

### Feature importance
Using the *explore* interface, Nkululeko analyses the importance of acoustic features:
 
![Feature importance](./images/feat_importance.png)

### Feature distribution
And can show the distribution of specific features per category:

![Feature distribution](./images/feat_dist.png)

If there are only two categories, a Mann-Whitney U test for significance is given:

![Feature distribution for two categories](./images/feat_dist_2.png)

### t-SNE plots
A t-SNE plot can give you an estimate of whether your acoustic features are useful at all.
Your feature space gets tranferred into 2 or 3-dimensional space and the color of the blobs reflect your target class distribution.
Beneath t-SNE, [you can choose from UMAP and PCA](https://github.com/felixbur/nkululeko/blob/main/ini_file.md#expl). 

![t-SNE plot](./images/tsne.png)

### Data distribution
Sometimes, you only want to take a look at your data. In this example, you see that the biological sex of the speakers is approximately uniformly distributed with respect to some emotional speech database.

![Data distribution](./images/data_plot.png)

### Bias checking
In some cases, you might wonder if there's bias in your data. You can try to detect this with automatically estimated speech properties by visualizing the correlation of target labels and predicted labels.

![Bias checking](./images/emotion-pesq.png)

### Uncertainty
Nkululeko estimates the uncertainty of model decisions (only for classifiers) with entropy over the class probabilities or logits per sample.

![Uncertainty](./images/uncertainty.png)