|
--- |
|
license: mit |
|
library_name: sklearn |
|
tags: |
|
- sklearn |
|
- skops |
|
- tabular-classification |
|
model_format: skops |
|
model_file: local_compartment_classifier_bd_boxes.skops |
|
widget: |
|
- structuredData: |
|
area_nm2: |
|
- 693824.0 |
|
- 4852608.0 |
|
- 17088896.0 |
|
area_nm2_neighbor_mean: |
|
- 10181485.714285716 |
|
- 9884429.714285716 |
|
- 9010409.142857144 |
|
area_nm2_neighbor_std: |
|
- 8312409.263207569 |
|
- 8587259.418816902 |
|
- 8418630.640116522 |
|
max_dt_nm: |
|
- 69.0 |
|
- 543.0 |
|
- 1287.0 |
|
max_dt_nm_neighbor_mean: |
|
- 664.7142857142857 |
|
- 630.8571428571429 |
|
- 577.7142857142857 |
|
max_dt_nm_neighbor_std: |
|
- 479.64240342658945 |
|
- 504.9563358340017 |
|
- 468.41868657651344 |
|
mean_dt_nm: |
|
- 24.4375 |
|
- 156.5 |
|
- 416.0 |
|
mean_dt_nm_neighbor_mean: |
|
- 198.62946428571428 |
|
- 189.19642857142856 |
|
- 170.66071428571428 |
|
mean_dt_nm_neighbor_std: |
|
- 150.614304054458 |
|
- 157.4368957825056 |
|
- 143.32375093543624 |
|
pca_ratio_01: |
|
- 1.3849340770961909 |
|
- 1.181656878273399 |
|
- 1.128046800200765 |
|
pca_ratio_01_neighbor_mean: |
|
- 1.8575624906424115 |
|
- 1.8760422359899387 |
|
- 1.880915879451087 |
|
pca_ratio_01_neighbor_std: |
|
- 0.641580757345606 |
|
- 0.6228187048854344 |
|
- 0.6165585104590592 |
|
pca_unwrapped_0: |
|
- -0.0046539306640625 |
|
- -0.497314453125 |
|
- -0.258544921875 |
|
pca_unwrapped_0_neighbor_mean: |
|
- 0.039224624633789 |
|
- 0.0840119448575106 |
|
- 0.0623056238347833 |
|
pca_unwrapped_0_neighbor_std: |
|
- 0.3114910605258688 |
|
- 0.2573427692683507 |
|
- 0.296254177168357 |
|
pca_unwrapped_1: |
|
- 0.7392578125 |
|
- -0.11553955078125 |
|
- 0.2169189453125 |
|
pca_unwrapped_1_neighbor_mean: |
|
- 0.0941687497225674 |
|
- 0.1718776009299538 |
|
- 0.1416541012850674 |
|
pca_unwrapped_1_neighbor_std: |
|
- 0.3179467337379631 |
|
- 0.3628551035117971 |
|
- 0.372447324946889 |
|
pca_unwrapped_2: |
|
- -0.673828125 |
|
- -0.85986328125 |
|
- 0.94140625 |
|
pca_unwrapped_2_neighbor_mean: |
|
- 0.2258744673295454 |
|
- 0.2427867542613636 |
|
- 0.0790349786931818 |
|
pca_unwrapped_2_neighbor_std: |
|
- 0.9134250264562896 |
|
- 0.8928014788058292 |
|
- 0.9167197839332804 |
|
pca_unwrapped_3: |
|
- -0.0302886962890625 |
|
- -0.86572265625 |
|
- 0.57177734375 |
|
pca_unwrapped_3_neighbor_mean: |
|
- -0.2933238636363636 |
|
- -0.2173753218217329 |
|
- -0.3480571400035511 |
|
pca_unwrapped_3_neighbor_std: |
|
- 0.6203425764161097 |
|
- 0.5938304683645145 |
|
- 0.5600074530240728 |
|
pca_unwrapped_4: |
|
- 0.67333984375 |
|
- -0.0005474090576171 |
|
- 0.81982421875 |
|
pca_unwrapped_4_neighbor_mean: |
|
- 0.2915762121027166 |
|
- 0.3528386896306818 |
|
- 0.2782594507390802 |
|
pca_unwrapped_4_neighbor_std: |
|
- 0.6415192812587974 |
|
- 0.6430080201673403 |
|
- 0.6308895861182334 |
|
pca_unwrapped_5: |
|
- 0.73876953125 |
|
- 0.50048828125 |
|
- -0.03192138671875 |
|
pca_unwrapped_5_neighbor_mean: |
|
- 0.2028697620738636 |
|
- 0.2245316938920454 |
|
- 0.2729325727982954 |
|
pca_unwrapped_5_neighbor_std: |
|
- 0.265173781606759 |
|
- 0.2994363858938455 |
|
- 0.2968562365279343 |
|
pca_unwrapped_6: |
|
- 0.99951171875 |
|
- 0.05828857421875 |
|
- -0.77880859375 |
|
pca_unwrapped_6_neighbor_mean: |
|
- -0.2386505820534446 |
|
- -0.1530848416415128 |
|
- -0.0769850990988991 |
|
pca_unwrapped_6_neighbor_std: |
|
- 0.6776577717043619 |
|
- 0.7717860533115238 |
|
- 0.7447135522384378 |
|
pca_unwrapped_7: |
|
- 0.023834228515625 |
|
- -0.9931640625 |
|
- 0.52978515625 |
|
pca_unwrapped_7_neighbor_mean: |
|
- -0.4803272594105113 |
|
- -0.3878728693181818 |
|
- -0.5263227982954546 |
|
pca_unwrapped_7_neighbor_std: |
|
- 0.4799926318285017 |
|
- 0.4691567465869561 |
|
- 0.3891669942534205 |
|
pca_unwrapped_8: |
|
- 0.0192413330078125 |
|
- 0.0997314453125 |
|
- -0.3359375 |
|
pca_unwrapped_8_neighbor_mean: |
|
- -0.0384375832297585 |
|
- -0.0457548661665482 |
|
- -0.0061485984108664 |
|
pca_unwrapped_8_neighbor_std: |
|
- 0.3037878488292577 |
|
- 0.3010843368506175 |
|
- 0.2874409267860334 |
|
pca_val_unwrapped_0: |
|
- 15657.09765625 |
|
- 40668.40625 |
|
- 66863.0 |
|
pca_val_unwrapped_0_neighbor_mean: |
|
- 69378.52059659091 |
|
- 67104.76526988637 |
|
- 64723.43856534091 |
|
pca_val_unwrapped_0_neighbor_std: |
|
- 20242.245019019712 |
|
- 24702.906417865197 |
|
- 25959.16138296664 |
|
pca_val_unwrapped_1: |
|
- 11305.3017578125 |
|
- 34416.42578125 |
|
- 59273.25 |
|
pca_val_unwrapped_1_neighbor_mean: |
|
- 41190.40261008523 |
|
- 39089.39133522727 |
|
- 36829.68004261364 |
|
pca_val_unwrapped_1_neighbor_std: |
|
- 16625.870141811894 |
|
- 18875.56976212627 |
|
- 17666.778281657556 |
|
pca_val_unwrapped_2: |
|
- 1270.4095458984375 |
|
- 13551.6748046875 |
|
- 47764.625 |
|
pca_val_unwrapped_2_neighbor_mean: |
|
- 28717.50048828125 |
|
- 27601.021828391335 |
|
- 24490.75362881747 |
|
pca_val_unwrapped_2_neighbor_std: |
|
- 14988.204981576571 |
|
- 16601.48080038032 |
|
- 15622.078784778376 |
|
post_synapse_count: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
post_synapse_count_neighbor_mean: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
post_synapse_count_neighbor_std: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
pre_synapse_count: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
pre_synapse_count_neighbor_mean: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
pre_synapse_count_neighbor_std: |
|
- 0.0 |
|
- 0.0 |
|
- 0.0 |
|
size_nm3: |
|
- 12771840.0 |
|
- 697943040.0 |
|
- 7550330880.0 |
|
size_nm3_neighbor_mean: |
|
- 3233702034.285714 |
|
- 3184761234.285714 |
|
- 2695304960.0 |
|
size_nm3_neighbor_std: |
|
- 3650678969.7909584 |
|
- 3691650923.5639486 |
|
- 3518520747.0511127 |
|
--- |
|
|
|
# Model description |
|
|
|
This is a model trained to classify pieces of neuron as axon, dendrite, soma, or glia, |
|
based only on their local shape and synapse features.The model is a linear discriminant |
|
classifier which was trained on compartment labels generated by Bethanny Danskin for |
|
3 6x6x6 um boxes in the Minnie65 Phase3 dataset. |
|
|
|
## Intended uses & limitations |
|
|
|
This model could be used to predict some compartment labels in mouse cortical |
|
connectomes, but it is unclear to what extent this model will generalize. |
|
|
|
## Training Procedure |
|
|
|
The model was trained on local (level 2 cache) and synapse count features from 3 6x6x6 |
|
um boxes in the Minnie65 Phase3 dataset. These features were also locally aggregated in |
|
5-hop neighborhood windows and concatenated to each level 2 node's features. The labels |
|
were generated by Bethanny Danskin and include axon, dendrite, soma, and glia |
|
compartments. The classification model was trained using a linear discriminant |
|
classifier. |
|
|
|
### Hyperparameters |
|
|
|
<details> |
|
<summary> Click to expand </summary> |
|
|
|
| Hyperparameter | Value | |
|
| ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------- | |
|
| memory | | |
|
| steps | [('transformer', QuantileTransformer(output_distribution='normal')), ('lda', LinearDiscriminantAnalysis(n_components=3))] | |
|
| verbose | False | |
|
| transformer | QuantileTransformer(output_distribution='normal') | |
|
| lda | LinearDiscriminantAnalysis(n_components=3) | |
|
| transformer\_\_copy | True | |
|
| transformer\_\_ignore_implicit_zeros | False | |
|
| transformer\_\_n_quantiles | 1000 | |
|
| transformer\_\_output_distribution | normal | |
|
| transformer\_\_random_state | | |
|
| transformer\_\_subsample | 10000 | |
|
| lda\_\_covariance_estimator | | |
|
| lda\_\_n_components | 3 | |
|
| lda\_\_priors | | |
|
| lda\_\_shrinkage | | |
|
| lda\_\_solver | svd | |
|
| lda\_\_store_covariance | False | |
|
| lda\_\_tol | 0.0001 | |
|
|
|
</details> |
|
|
|
### Model Plot |
|
|
|
<style>#sk-container-id-9 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;} |
|
}#sk-container-id-9 {color: var(--sklearn-color-text); |
|
}#sk-container-id-9 pre {padding: 0; |
|
}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px; |
|
}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background); |
|
}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative; |
|
}#sk-container-id-9 div.sk-text-repr-fallback {display: none; |
|
}div.sk-parallel-item, |
|
div.sk-serial, |
|
div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center; |
|
}/* Parallel-specific style estimator block */#sk-container-id-9 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1; |
|
}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative; |
|
}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column; |
|
}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%; |
|
}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%; |
|
}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0; |
|
}/* Serial-specific style estimator block */#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em; |
|
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is |
|
clickable and can be expanded/collapsed. |
|
- Pipeline and ColumnTransformer use this feature and define the default style |
|
- Estimators will overwrite some part of the style using the `sk-estimator` class |
|
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-9 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background); |
|
}/* Toggleable label */ |
|
#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center; |
|
}#sk-container-id-9 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon); |
|
}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text); |
|
}/* Toggleable content - dropdown */#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
|
}#sk-container-id-9 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
|
}#sk-container-id-9 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto; |
|
}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾"; |
|
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); |
|
}#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2); |
|
}/* Estimator-specific style *//* Colorize estimator box */ |
|
#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
|
}#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
|
}#sk-container-id-9 div.sk-label label.sk-toggleable__label, |
|
#sk-container-id-9 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background); |
|
}/* On hover, darken the color of the background */ |
|
#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2); |
|
}/* Label box, darken color on hover, fitted */ |
|
#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2); |
|
}/* Estimator label */#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em; |
|
}#sk-container-id-9 div.sk-label-container {text-align: center; |
|
}/* Estimator-specific */ |
|
#sk-container-id-9 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0); |
|
}#sk-container-id-9 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0); |
|
}/* on hover */ |
|
#sk-container-id-9 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2); |
|
}#sk-container-id-9 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2); |
|
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link, |
|
a:link.sk-estimator-doc-link, |
|
a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1); |
|
}.sk-estimator-doc-link.fitted, |
|
a:link.sk-estimator-doc-link.fitted, |
|
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
|
}/* On hover */ |
|
div.sk-estimator:hover .sk-estimator-doc-link:hover, |
|
.sk-estimator-doc-link:hover, |
|
div.sk-label-container:hover .sk-estimator-doc-link:hover, |
|
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
|
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover, |
|
.sk-estimator-doc-link.fitted:hover, |
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover, |
|
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
|
}/* Span, style for the box shown on hovering the info icon */ |
|
.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3); |
|
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3); |
|
}.sk-estimator-doc-link:hover span {display: block; |
|
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-9 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid; |
|
}#sk-container-id-9 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1); |
|
}/* On hover */ |
|
#sk-container-id-9 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none; |
|
}#sk-container-id-9 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3); |
|
} |
|
</style><div id="sk-container-id-9" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('transformer',QuantileTransformer(output_distribution='normal')),('lda', LinearDiscriminantAnalysis(n_components=3))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('transformer',QuantileTransformer(output_distribution='normal')),('lda', LinearDiscriminantAnalysis(n_components=3))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> QuantileTransformer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.QuantileTransformer.html">?<span>Documentation for QuantileTransformer</span></a></label><div class="sk-toggleable__content fitted"><pre>QuantileTransformer(output_distribution='normal')</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> LinearDiscriminantAnalysis<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html">?<span>Documentation for LinearDiscriminantAnalysis</span></a></label><div class="sk-toggleable__content fitted"><pre>LinearDiscriminantAnalysis(n_components=3)</pre></div> </div></div></div></div></div></div> |
|
|
|
## Evaluation Results |
|
|
|
### Classification Report (overall) |
|
|
|
| type | precision | recall | f1-score | support | |
|
| ------------ | --------- | -------- | -------- | -------- | |
|
| accuracy | 0.944357 | 0.944357 | 0.944357 | 0.944357 | |
|
| macro avg | 0.854825 | 0.917289 | 0.878753 | 31307 | |
|
| weighted avg | 0.946879 | 0.944357 | 0.945155 | 31307 | |
|
|
|
### Classification Report (by class) |
|
|
|
| class | precision | recall | f1-score | support | |
|
| -------- | --------- | -------- | -------- | ------- | |
|
| axon | 0.956309 | 0.964704 | 0.960488 | 16404 | |
|
| dendrite | 0.928038 | 0.911341 | 0.919614 | 6948 | |
|
| glia | 0.964442 | 0.935279 | 0.949636 | 7540 | |
|
| soma | 0.570513 | 0.857831 | 0.685274 | 415 | |
|
|
|
# How to Get Started with the Model |
|
|
|
[More Information Needed] |
|
|
|
# Model Card Authors |
|
|
|
Ben Pedigo |
|
Bethanny Danskin |
|
|
