added linebreak
Browse files
app.py
CHANGED
|
@@ -100,7 +100,7 @@ with gr.Blocks(title=title) as demo:
|
|
| 100 |
" PCA applied to this data identifies the combination of attributes (principal components, or directions in the feature space) that account for the most variance in the data. Here we plot the different samples on the 2 first principal components. <br>"
|
| 101 |
" LDA is a supervised method that tries to identify attributes that account for the most variance between classes using the known class labels. <br>"
|
| 102 |
" The number of samples (n_samples) will determine the number of data points to produce. <br>"
|
| 103 |
-
" The number of components is fixed to 2 for this 2-D visualisation and LDA requires the number of components to be the number of classes -1, which in this case is (3-1) = 2"
|
| 104 |
" The number of features (n_features) determine the number of features from the IRIS dataset to use for the model fitting. <br>"
|
| 105 |
" For further details please see the sklearn docs:"
|
| 106 |
)
|
|
|
|
| 100 |
" PCA applied to this data identifies the combination of attributes (principal components, or directions in the feature space) that account for the most variance in the data. Here we plot the different samples on the 2 first principal components. <br>"
|
| 101 |
" LDA is a supervised method that tries to identify attributes that account for the most variance between classes using the known class labels. <br>"
|
| 102 |
" The number of samples (n_samples) will determine the number of data points to produce. <br>"
|
| 103 |
+
" The number of components is fixed to 2 for this 2-D visualisation and LDA requires the number of components to be the number of classes -1, which in this case is (3-1) = 2. <br>"
|
| 104 |
" The number of features (n_features) determine the number of features from the IRIS dataset to use for the model fitting. <br>"
|
| 105 |
" For further details please see the sklearn docs:"
|
| 106 |
)
|