pushing files to the repo from the example!
Browse files- README.md +67 -67
- config.json +64 -64
- confusion_matrix.png +0 -0
- model.pkl +1 -1
- tree.png +0 -0
README.md
CHANGED
|
@@ -12,96 +12,96 @@ widget:
|
|
| 12 |
- material_7
|
| 13 |
attribute_1:
|
| 14 |
- material_8
|
| 15 |
-
- material_6
|
| 16 |
- material_8
|
|
|
|
| 17 |
attribute_2:
|
| 18 |
- 9
|
|
|
|
| 19 |
- 6
|
| 20 |
-
- 5
|
| 21 |
attribute_3:
|
| 22 |
- 5
|
| 23 |
-
-
|
| 24 |
-
-
|
| 25 |
loading:
|
| 26 |
-
-
|
| 27 |
-
-
|
| 28 |
-
-
|
| 29 |
measurement_0:
|
|
|
|
| 30 |
- 11
|
| 31 |
-
-
|
| 32 |
-
- 24
|
| 33 |
measurement_1:
|
| 34 |
-
- 2
|
| 35 |
-
- 8
|
| 36 |
- 7
|
|
|
|
|
|
|
| 37 |
measurement_10:
|
| 38 |
-
-
|
| 39 |
-
- 15.
|
| 40 |
-
-
|
| 41 |
measurement_11:
|
| 42 |
-
-
|
| 43 |
-
-
|
| 44 |
-
- 20.
|
| 45 |
measurement_12:
|
| 46 |
-
- 12.
|
| 47 |
-
- .
|
| 48 |
-
-
|
| 49 |
measurement_13:
|
| 50 |
-
-
|
| 51 |
-
-
|
| 52 |
-
- .
|
| 53 |
measurement_14:
|
| 54 |
-
-
|
| 55 |
-
- .
|
| 56 |
-
-
|
| 57 |
measurement_15:
|
| 58 |
-
-
|
| 59 |
-
- 15.
|
| 60 |
-
-
|
| 61 |
measurement_16:
|
| 62 |
-
-
|
| 63 |
-
-
|
| 64 |
-
- 17.
|
| 65 |
measurement_17:
|
| 66 |
-
-
|
| 67 |
-
-
|
| 68 |
-
-
|
| 69 |
measurement_2:
|
| 70 |
-
-
|
| 71 |
-
-
|
| 72 |
-
-
|
| 73 |
measurement_3:
|
| 74 |
-
-
|
| 75 |
-
-
|
| 76 |
-
-
|
| 77 |
measurement_4:
|
| 78 |
-
-
|
| 79 |
-
-
|
| 80 |
-
-
|
| 81 |
measurement_5:
|
| 82 |
-
-
|
| 83 |
-
-
|
| 84 |
-
-
|
| 85 |
measurement_6:
|
| 86 |
-
-
|
| 87 |
-
- 17.
|
| 88 |
-
- 17.
|
| 89 |
measurement_7:
|
| 90 |
-
-
|
| 91 |
-
- 11.
|
| 92 |
-
-
|
| 93 |
measurement_8:
|
| 94 |
-
-
|
| 95 |
-
-
|
| 96 |
-
-
|
| 97 |
measurement_9:
|
| 98 |
-
-
|
| 99 |
-
-
|
| 100 |
-
-
|
| 101 |
product_code:
|
| 102 |
- A
|
| 103 |
-
-
|
| 104 |
-
-
|
| 105 |
---
|
| 106 |
|
| 107 |
# Model description
|
|
@@ -220,7 +220,7 @@ The model is trained with below hyperparameters.
|
|
| 220 |
|
| 221 |
The model plot is below.
|
| 222 |
|
| 223 |
-
<style>#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 {color: black;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 pre{padding: 0;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable {background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-item {z-index: 1;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-parallel-item:only-child::after {width: 0;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 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 the default 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-b89bd1e9-e872-49aa-bc53-73dc52fc4e14 div.sk-text-repr-fallback {display: none;}</style><div id="sk-b89bd1e9-e872-49aa-bc53-73dc52fc4e14" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="82f19dd0-da3e-499c-84b9-f67ed489906d" type="checkbox" ><label for="82f19dd0-da3e-499c-84b9-f67ed489906d" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e3bc6996-eefc-4601-a7df-7850743b36d6" type="checkbox" ><label for="e3bc6996-eefc-4601-a7df-7850743b36d6" class="sk-toggleable__label sk-toggleable__label-arrow">transformation: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(), ['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3', 'measurement_4','measurement_5', 'measurement_6','measurement_7', 'measurement_8','measurement_9', 'measurement_10','measurement_11', 'measurement_12','measurement_13', 'measurement_14','measurement_15', 'measurement_16','measurement_17']),('attribute_0_encoder', OneHotEncoder(),['attribute_0']),('attribute_1_encoder', OneHotEncoder(),['attribute_1']),('product_code_encoder', OneHotEncoder(),['product_code'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9cc3e3ef-17e8-4bf4-a121-0a29a377373e" type="checkbox" ><label for="9cc3e3ef-17e8-4bf4-a121-0a29a377373e" class="sk-toggleable__label sk-toggleable__label-arrow">loading_missing_value_imputer</label><div class="sk-toggleable__content"><pre>['loading']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e7ba5c2d-3bf3-444f-be00-0ecf4b563cd7" type="checkbox" ><label for="e7ba5c2d-3bf3-444f-be00-0ecf4b563cd7" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2277368d-30f2-46c1-a283-9f0ccf350872" type="checkbox" ><label for="2277368d-30f2-46c1-a283-9f0ccf350872" class="sk-toggleable__label sk-toggleable__label-arrow">numerical_missing_value_imputer</label><div class="sk-toggleable__content"><pre>['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="2a49159e-c23f-4cbe-92bb-09bb64c1354d" type="checkbox" ><label for="2a49159e-c23f-4cbe-92bb-09bb64c1354d" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c87d52bb-0b23-4e43-abe8-afc3759dac02" type="checkbox" ><label for="c87d52bb-0b23-4e43-abe8-afc3759dac02" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_0_encoder</label><div class="sk-toggleable__content"><pre>['attribute_0']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="023971df-ed99-4eaf-8f0d-cd115bacbb45" type="checkbox" ><label for="023971df-ed99-4eaf-8f0d-cd115bacbb45" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="111f5303-3f63-409a-9dc1-74ab94419974" type="checkbox" ><label for="111f5303-3f63-409a-9dc1-74ab94419974" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_1_encoder</label><div class="sk-toggleable__content"><pre>['attribute_1']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c858e1b1-b68f-4700-9111-32772a7b51ab" type="checkbox" ><label for="c858e1b1-b68f-4700-9111-32772a7b51ab" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5ce65801-d4be-48d4-81d3-7998e483cf65" type="checkbox" ><label for="5ce65801-d4be-48d4-81d3-7998e483cf65" class="sk-toggleable__label sk-toggleable__label-arrow">product_code_encoder</label><div class="sk-toggleable__content"><pre>['product_code']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="75a1cce7-c7f7-41cf-bb4d-8e403291a41b" type="checkbox" ><label for="75a1cce7-c7f7-41cf-bb4d-8e403291a41b" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3c311565-4080-492c-b353-fbc41e1c17d5" type="checkbox" ><label for="3c311565-4080-492c-b353-fbc41e1c17d5" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div></div></div>
|
| 224 |
|
| 225 |
## Evaluation Results
|
| 226 |
|
|
@@ -230,8 +230,8 @@ You can find the details about evaluation process and the evaluation results.
|
|
| 230 |
|
| 231 |
| Metric | Value |
|
| 232 |
|----------|----------|
|
| 233 |
-
| accuracy | 0.
|
| 234 |
-
| f1 score | 0.
|
| 235 |
|
| 236 |
# How to Get Started with the Model
|
| 237 |
|
|
@@ -242,7 +242,7 @@ Use the code below to get started with the model.
|
|
| 242 |
|
| 243 |
```python
|
| 244 |
import pickle
|
| 245 |
-
with open(model.pkl, 'rb') as file:
|
| 246 |
clf = pickle.load(file)
|
| 247 |
```
|
| 248 |
|
|
@@ -273,9 +273,9 @@ Below you can find information related to citation.
|
|
| 273 |
|
| 274 |
|
| 275 |
Tree Plot
|
| 276 |
-
|
| 277 |
|
| 278 |
|
| 279 |
|
| 280 |
Confusion Matrix
|
| 281 |
-
|
|
|
|
| 12 |
- material_7
|
| 13 |
attribute_1:
|
| 14 |
- material_8
|
|
|
|
| 15 |
- material_8
|
| 16 |
+
- material_5
|
| 17 |
attribute_2:
|
| 18 |
- 9
|
| 19 |
+
- 9
|
| 20 |
- 6
|
|
|
|
| 21 |
attribute_3:
|
| 22 |
- 5
|
| 23 |
+
- 5
|
| 24 |
+
- 6
|
| 25 |
loading:
|
| 26 |
+
- 150.15
|
| 27 |
+
- 106.3
|
| 28 |
+
- 117.52
|
| 29 |
measurement_0:
|
| 30 |
+
- 6
|
| 31 |
- 11
|
| 32 |
+
- 4
|
|
|
|
| 33 |
measurement_1:
|
|
|
|
|
|
|
| 34 |
- 7
|
| 35 |
+
- 4
|
| 36 |
+
- 9
|
| 37 |
measurement_10:
|
| 38 |
+
- 15.888
|
| 39 |
+
- 15.56
|
| 40 |
+
- 18.49
|
| 41 |
measurement_11:
|
| 42 |
+
- 21.623
|
| 43 |
+
- 17.233
|
| 44 |
+
- 20.193
|
| 45 |
measurement_12:
|
| 46 |
+
- 12.83
|
| 47 |
+
- 12.926
|
| 48 |
+
- 14.127
|
| 49 |
measurement_13:
|
| 50 |
+
- 14.738
|
| 51 |
+
- 14.367
|
| 52 |
+
- 15.185
|
| 53 |
measurement_14:
|
| 54 |
+
- 18.506
|
| 55 |
+
- 16.302
|
| 56 |
+
- 16.657
|
| 57 |
measurement_15:
|
| 58 |
+
- 14.16
|
| 59 |
+
- 15.018
|
| 60 |
+
- 13.326
|
| 61 |
measurement_16:
|
| 62 |
+
- 15.266
|
| 63 |
+
- 18.297
|
| 64 |
+
- 17.467
|
| 65 |
measurement_17:
|
| 66 |
+
- 674.165
|
| 67 |
+
- 604.836
|
| 68 |
+
- 648.023
|
| 69 |
measurement_2:
|
| 70 |
+
- 11
|
| 71 |
+
- 4
|
| 72 |
+
- 9
|
| 73 |
measurement_3:
|
| 74 |
+
- 19.637
|
| 75 |
+
- 18.217
|
| 76 |
+
- 19.325
|
| 77 |
measurement_4:
|
| 78 |
+
- 12.55
|
| 79 |
+
- 10.627
|
| 80 |
+
- 10.092
|
| 81 |
measurement_5:
|
| 82 |
+
- 17.119
|
| 83 |
+
- 17.74
|
| 84 |
+
- 17.218
|
| 85 |
measurement_6:
|
| 86 |
+
- .nan
|
| 87 |
+
- 17.295
|
| 88 |
+
- 17.962
|
| 89 |
measurement_7:
|
| 90 |
+
- 10.958
|
| 91 |
+
- 11.732
|
| 92 |
+
- 9.274
|
| 93 |
measurement_8:
|
| 94 |
+
- 17.93
|
| 95 |
+
- 17.591
|
| 96 |
+
- 18.653
|
| 97 |
measurement_9:
|
| 98 |
+
- .nan
|
| 99 |
+
- 12.689
|
| 100 |
+
- 13.149
|
| 101 |
product_code:
|
| 102 |
- A
|
| 103 |
+
- A
|
| 104 |
+
- D
|
| 105 |
---
|
| 106 |
|
| 107 |
# Model description
|
|
|
|
| 220 |
|
| 221 |
The model plot is below.
|
| 222 |
|
| 223 |
+
<style>#sk-cbcf73f3-3df0-460c-a28c-e975797de98c {color: black;background-color: white;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c pre{padding: 0;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-toggleable {background-color: white;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c 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-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-estimator:hover {background-color: #d4ebff;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-item {z-index: 1;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-parallel-item:only-child::after {width: 0;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-cbcf73f3-3df0-460c-a28c-e975797de98c 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 the default 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-cbcf73f3-3df0-460c-a28c-e975797de98c div.sk-text-repr-fallback {display: none;}</style><div id="sk-cbcf73f3-3df0-460c-a28c-e975797de98c" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4039f6df-38bb-4617-ac8b-f6e94de8a91c" type="checkbox" ><label for="4039f6df-38bb-4617-ac8b-f6e94de8a91c" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(),['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3','measurement_4','measurement_5','measurement_6','measurement_7','measurement_8','measurement_9','measurement_10','measurement_11','measurement_12','measurement_13','measurement_14','measurement_15','measurement_16','measurement_17']),('attribute_0_encoder',OneHotEncoder(),['attribute_0']),('attribute_1_encoder',OneHotEncoder(),['attribute_1']),('product_code_encoder',OneHotEncoder(),['product_code'])])),('model', DecisionTreeClassifier(max_depth=4))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="61e07386-e7b7-418a-9af8-41b0261577b4" type="checkbox" ><label for="61e07386-e7b7-418a-9af8-41b0261577b4" class="sk-toggleable__label sk-toggleable__label-arrow">transformation: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('loading_missing_value_imputer',SimpleImputer(), ['loading']),('numerical_missing_value_imputer',SimpleImputer(),['loading', 'measurement_3', 'measurement_4','measurement_5', 'measurement_6','measurement_7', 'measurement_8','measurement_9', 'measurement_10','measurement_11', 'measurement_12','measurement_13', 'measurement_14','measurement_15', 'measurement_16','measurement_17']),('attribute_0_encoder', OneHotEncoder(),['attribute_0']),('attribute_1_encoder', OneHotEncoder(),['attribute_1']),('product_code_encoder', OneHotEncoder(),['product_code'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="543953aa-7345-4433-b640-9ebcb9cfaed6" type="checkbox" ><label for="543953aa-7345-4433-b640-9ebcb9cfaed6" class="sk-toggleable__label sk-toggleable__label-arrow">loading_missing_value_imputer</label><div class="sk-toggleable__content"><pre>['loading']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="28f1b85a-54e9-44db-b914-819af4998fd1" type="checkbox" ><label for="28f1b85a-54e9-44db-b914-819af4998fd1" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d8710d93-e747-4796-95d5-77538856cb1d" type="checkbox" ><label for="d8710d93-e747-4796-95d5-77538856cb1d" class="sk-toggleable__label sk-toggleable__label-arrow">numerical_missing_value_imputer</label><div class="sk-toggleable__content"><pre>['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b23ea887-b3eb-4dbc-ba26-dd3e0e018c70" type="checkbox" ><label for="b23ea887-b3eb-4dbc-ba26-dd3e0e018c70" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c87a37af-e576-4840-bf8c-7e7f5b8ab39e" type="checkbox" ><label for="c87a37af-e576-4840-bf8c-7e7f5b8ab39e" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_0_encoder</label><div class="sk-toggleable__content"><pre>['attribute_0']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="580ea11e-4df6-4bce-b994-dc4d342d42d4" type="checkbox" ><label for="580ea11e-4df6-4bce-b994-dc4d342d42d4" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="8ccfa95c-d0f7-4dd2-8be2-0885a564d231" type="checkbox" ><label for="8ccfa95c-d0f7-4dd2-8be2-0885a564d231" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_1_encoder</label><div class="sk-toggleable__content"><pre>['attribute_1']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e1ed00d2-3cb6-43cd-9ba5-bc0518c93345" type="checkbox" ><label for="e1ed00d2-3cb6-43cd-9ba5-bc0518c93345" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="b94ddf76-0075-4efc-9cc4-8c6b69fefad5" type="checkbox" ><label for="b94ddf76-0075-4efc-9cc4-8c6b69fefad5" class="sk-toggleable__label sk-toggleable__label-arrow">product_code_encoder</label><div class="sk-toggleable__content"><pre>['product_code']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="1d06bc4d-04b9-44d6-a23f-cdc26d70b7e2" type="checkbox" ><label for="1d06bc4d-04b9-44d6-a23f-cdc26d70b7e2" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="208c2a51-a582-469b-9bd1-23b9a3968840" type="checkbox" ><label for="208c2a51-a582-469b-9bd1-23b9a3968840" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div></div></div>
|
| 224 |
|
| 225 |
## Evaluation Results
|
| 226 |
|
|
|
|
| 230 |
|
| 231 |
| Metric | Value |
|
| 232 |
|----------|----------|
|
| 233 |
+
| accuracy | 0.778564 |
|
| 234 |
+
| f1 score | 0.778564 |
|
| 235 |
|
| 236 |
# How to Get Started with the Model
|
| 237 |
|
|
|
|
| 242 |
|
| 243 |
```python
|
| 244 |
import pickle
|
| 245 |
+
with open(decision-tree-playground-kaggle/model.pkl, 'rb') as file:
|
| 246 |
clf = pickle.load(file)
|
| 247 |
```
|
| 248 |
|
|
|
|
| 273 |
|
| 274 |
|
| 275 |
Tree Plot
|
| 276 |
+
|
| 277 |
|
| 278 |
|
| 279 |
|
| 280 |
Confusion Matrix
|
| 281 |
+
|
config.json
CHANGED
|
@@ -37,118 +37,118 @@
|
|
| 37 |
],
|
| 38 |
"attribute_1": [
|
| 39 |
"material_8",
|
| 40 |
-
"
|
| 41 |
-
"
|
| 42 |
],
|
| 43 |
"attribute_2": [
|
| 44 |
9,
|
| 45 |
-
|
| 46 |
-
|
| 47 |
],
|
| 48 |
"attribute_3": [
|
| 49 |
5,
|
| 50 |
-
|
| 51 |
-
|
| 52 |
],
|
| 53 |
"loading": [
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
|
| 57 |
],
|
| 58 |
"measurement_0": [
|
|
|
|
| 59 |
11,
|
| 60 |
-
|
| 61 |
-
24
|
| 62 |
],
|
| 63 |
"measurement_1": [
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
|
| 67 |
],
|
| 68 |
"measurement_10": [
|
| 69 |
-
|
| 70 |
-
15.
|
| 71 |
-
|
| 72 |
],
|
| 73 |
"measurement_11": [
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
20.
|
| 77 |
],
|
| 78 |
"measurement_12": [
|
| 79 |
-
12.
|
| 80 |
-
|
| 81 |
-
|
| 82 |
],
|
| 83 |
"measurement_13": [
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
|
| 87 |
],
|
| 88 |
"measurement_14": [
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
|
| 92 |
],
|
| 93 |
"measurement_15": [
|
| 94 |
-
|
| 95 |
-
15.
|
| 96 |
-
|
| 97 |
],
|
| 98 |
"measurement_16": [
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
17.
|
| 102 |
],
|
| 103 |
"measurement_17": [
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
|
| 107 |
],
|
| 108 |
"measurement_2": [
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
|
| 112 |
],
|
| 113 |
"measurement_3": [
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
|
| 117 |
],
|
| 118 |
"measurement_4": [
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
|
| 122 |
],
|
| 123 |
"measurement_5": [
|
| 124 |
-
|
| 125 |
-
|
| 126 |
-
|
| 127 |
],
|
| 128 |
"measurement_6": [
|
| 129 |
-
|
| 130 |
-
17.
|
| 131 |
-
17.
|
| 132 |
],
|
| 133 |
"measurement_7": [
|
| 134 |
-
|
| 135 |
-
11.
|
| 136 |
-
|
| 137 |
],
|
| 138 |
"measurement_8": [
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
|
| 142 |
],
|
| 143 |
"measurement_9": [
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
|
| 147 |
],
|
| 148 |
"product_code": [
|
| 149 |
"A",
|
| 150 |
-
"
|
| 151 |
-
"
|
| 152 |
]
|
| 153 |
},
|
| 154 |
"model": {
|
|
|
|
| 37 |
],
|
| 38 |
"attribute_1": [
|
| 39 |
"material_8",
|
| 40 |
+
"material_8",
|
| 41 |
+
"material_5"
|
| 42 |
],
|
| 43 |
"attribute_2": [
|
| 44 |
9,
|
| 45 |
+
9,
|
| 46 |
+
6
|
| 47 |
],
|
| 48 |
"attribute_3": [
|
| 49 |
5,
|
| 50 |
+
5,
|
| 51 |
+
6
|
| 52 |
],
|
| 53 |
"loading": [
|
| 54 |
+
150.15,
|
| 55 |
+
106.3,
|
| 56 |
+
117.52
|
| 57 |
],
|
| 58 |
"measurement_0": [
|
| 59 |
+
6,
|
| 60 |
11,
|
| 61 |
+
4
|
|
|
|
| 62 |
],
|
| 63 |
"measurement_1": [
|
| 64 |
+
7,
|
| 65 |
+
4,
|
| 66 |
+
9
|
| 67 |
],
|
| 68 |
"measurement_10": [
|
| 69 |
+
15.888,
|
| 70 |
+
15.56,
|
| 71 |
+
18.49
|
| 72 |
],
|
| 73 |
"measurement_11": [
|
| 74 |
+
21.623,
|
| 75 |
+
17.233,
|
| 76 |
+
20.193
|
| 77 |
],
|
| 78 |
"measurement_12": [
|
| 79 |
+
12.83,
|
| 80 |
+
12.926,
|
| 81 |
+
14.127
|
| 82 |
],
|
| 83 |
"measurement_13": [
|
| 84 |
+
14.738,
|
| 85 |
+
14.367,
|
| 86 |
+
15.185
|
| 87 |
],
|
| 88 |
"measurement_14": [
|
| 89 |
+
18.506,
|
| 90 |
+
16.302,
|
| 91 |
+
16.657
|
| 92 |
],
|
| 93 |
"measurement_15": [
|
| 94 |
+
14.16,
|
| 95 |
+
15.018,
|
| 96 |
+
13.326
|
| 97 |
],
|
| 98 |
"measurement_16": [
|
| 99 |
+
15.266,
|
| 100 |
+
18.297,
|
| 101 |
+
17.467
|
| 102 |
],
|
| 103 |
"measurement_17": [
|
| 104 |
+
674.165,
|
| 105 |
+
604.836,
|
| 106 |
+
648.023
|
| 107 |
],
|
| 108 |
"measurement_2": [
|
| 109 |
+
11,
|
| 110 |
+
4,
|
| 111 |
+
9
|
| 112 |
],
|
| 113 |
"measurement_3": [
|
| 114 |
+
19.637,
|
| 115 |
+
18.217,
|
| 116 |
+
19.325
|
| 117 |
],
|
| 118 |
"measurement_4": [
|
| 119 |
+
12.55,
|
| 120 |
+
10.627,
|
| 121 |
+
10.092
|
| 122 |
],
|
| 123 |
"measurement_5": [
|
| 124 |
+
17.119,
|
| 125 |
+
17.74,
|
| 126 |
+
17.218
|
| 127 |
],
|
| 128 |
"measurement_6": [
|
| 129 |
+
NaN,
|
| 130 |
+
17.295,
|
| 131 |
+
17.962
|
| 132 |
],
|
| 133 |
"measurement_7": [
|
| 134 |
+
10.958,
|
| 135 |
+
11.732,
|
| 136 |
+
9.274
|
| 137 |
],
|
| 138 |
"measurement_8": [
|
| 139 |
+
17.93,
|
| 140 |
+
17.591,
|
| 141 |
+
18.653
|
| 142 |
],
|
| 143 |
"measurement_9": [
|
| 144 |
+
NaN,
|
| 145 |
+
12.689,
|
| 146 |
+
13.149
|
| 147 |
],
|
| 148 |
"product_code": [
|
| 149 |
"A",
|
| 150 |
+
"A",
|
| 151 |
+
"D"
|
| 152 |
]
|
| 153 |
},
|
| 154 |
"model": {
|
confusion_matrix.png
CHANGED
|
|
model.pkl
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 6824
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f40e9887075f5b2d676c4403570f5209241753a50303b4bfadeb32203376755a
|
| 3 |
size 6824
|
tree.png
CHANGED
|
|