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Browse files- Maternal_Health_Risk.csv +204 -0
- README.md +186 -0
- config.json +1 -0
- model.pkl +3 -0
Maternal_Health_Risk.csv
ADDED
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@@ -0,0 +1,204 @@
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| 1 |
+
,Age,SystolicBP,DiastolicBP,BS,BodyTemp,HeartRate,RiskLevel
|
| 2 |
+
1011,35,85,60,19.0,98.0,86,high risk
|
| 3 |
+
508,32,140,100,7.9,98.0,78,high risk
|
| 4 |
+
463,15,76,49,6.8,98.0,77,low risk
|
| 5 |
+
623,23,90,60,7.5,98.0,76,low risk
|
| 6 |
+
949,59,120,80,7.5,98.0,70,low risk
|
| 7 |
+
447,15,76,49,6.8,98.0,77,low risk
|
| 8 |
+
886,15,76,49,6.8,98.0,77,low risk
|
| 9 |
+
598,40,120,85,15.0,98.0,60,high risk
|
| 10 |
+
701,15,90,60,6.0,98.0,80,low risk
|
| 11 |
+
858,18,120,80,6.9,102.0,76,mid risk
|
| 12 |
+
808,23,120,90,7.9,98.0,70,mid risk
|
| 13 |
+
728,20,110,60,7.0,100.0,70,mid risk
|
| 14 |
+
177,54,140,100,15.0,98.0,66,high risk
|
| 15 |
+
579,15,120,80,7.5,98.0,70,low risk
|
| 16 |
+
11,19,120,80,7.0,98.0,70,mid risk
|
| 17 |
+
638,29,90,70,11.0,100.0,80,high risk
|
| 18 |
+
531,15,120,80,7.5,98.0,70,mid risk
|
| 19 |
+
218,31,120,60,6.1,98.0,76,mid risk
|
| 20 |
+
873,19,120,80,7.0,98.0,70,mid risk
|
| 21 |
+
358,18,90,60,6.9,98.0,70,mid risk
|
| 22 |
+
196,31,120,60,6.1,98.0,76,mid risk
|
| 23 |
+
16,50,140,90,15.0,98.0,90,high risk
|
| 24 |
+
719,29,130,70,6.1,98.0,78,mid risk
|
| 25 |
+
335,35,120,80,6.9,98.0,78,mid risk
|
| 26 |
+
425,35,100,60,15.0,98.0,80,high risk
|
| 27 |
+
696,23,90,60,6.7,98.0,76,low risk
|
| 28 |
+
142,17,90,63,6.9,101.0,70,mid risk
|
| 29 |
+
222,32,120,90,6.4,98.0,70,low risk
|
| 30 |
+
978,29,120,75,7.2,100.0,70,high risk
|
| 31 |
+
748,19,120,80,7.0,98.0,70,mid risk
|
| 32 |
+
891,18,120,80,6.8,102.0,76,low risk
|
| 33 |
+
318,54,130,70,12.0,98.0,67,mid risk
|
| 34 |
+
261,19,120,75,6.9,98.0,66,low risk
|
| 35 |
+
64,31,120,60,6.1,98.0,76,mid risk
|
| 36 |
+
667,15,90,49,6.0,98.0,77,low risk
|
| 37 |
+
948,17,90,65,7.5,103.0,67,low risk
|
| 38 |
+
280,60,120,80,7.7,98.0,75,low risk
|
| 39 |
+
573,42,120,80,7.5,98.0,70,low risk
|
| 40 |
+
619,29,130,70,7.5,98.0,78,mid risk
|
| 41 |
+
514,15,80,60,7.5,98.0,80,low risk
|
| 42 |
+
252,28,120,90,6.9,98.0,70,low risk
|
| 43 |
+
754,54,130,70,12.0,98.0,67,mid risk
|
| 44 |
+
855,20,120,75,7.01,100.0,70,mid risk
|
| 45 |
+
489,30,140,100,15.0,98.0,70,high risk
|
| 46 |
+
417,60,140,80,16.0,98.0,66,high risk
|
| 47 |
+
287,17,90,65,7.7,103.0,67,high risk
|
| 48 |
+
617,31,120,60,6.1,98.0,76,low risk
|
| 49 |
+
353,40,120,90,6.9,98.0,80,high risk
|
| 50 |
+
618,23,120,90,7.5,98.0,70,low risk
|
| 51 |
+
483,35,100,70,7.9,98.0,60,low risk
|
| 52 |
+
485,60,90,65,7.9,98.0,77,low risk
|
| 53 |
+
443,32,120,90,6.8,98.0,70,low risk
|
| 54 |
+
829,23,100,85,7.5,98.0,66,mid risk
|
| 55 |
+
139,18,120,80,6.9,102.0,76,mid risk
|
| 56 |
+
43,30,120,80,6.1,98.0,70,low risk
|
| 57 |
+
610,13,90,65,7.5,101.0,80,high risk
|
| 58 |
+
171,12,90,60,7.9,102.0,66,high risk
|
| 59 |
+
992,17,110,75,13.0,101.0,76,high risk
|
| 60 |
+
274,40,120,95,11.0,98.0,80,high risk
|
| 61 |
+
418,12,120,90,6.8,98.0,80,mid risk
|
| 62 |
+
408,12,120,95,6.8,98.0,60,mid risk
|
| 63 |
+
759,35,120,80,6.9,98.0,78,mid risk
|
| 64 |
+
718,31,120,60,6.1,98.0,76,mid risk
|
| 65 |
+
621,32,120,90,7.5,98.0,70,low risk
|
| 66 |
+
938,21,120,80,7.5,98.0,77,low risk
|
| 67 |
+
349,25,120,90,6.9,98.0,80,low risk
|
| 68 |
+
566,29,120,70,9.0,98.0,80,high risk
|
| 69 |
+
440,23,140,90,6.8,98.0,70,high risk
|
| 70 |
+
27,22,100,65,7.2,98.0,70,low risk
|
| 71 |
+
932,49,120,90,7.5,98.0,77,low risk
|
| 72 |
+
250,10,85,65,6.9,98.0,70,low risk
|
| 73 |
+
716,17,120,80,6.7,102.0,76,mid risk
|
| 74 |
+
375,60,120,80,7.8,98.0,75,high risk
|
| 75 |
+
721,28,85,60,9.0,101.0,86,mid risk
|
| 76 |
+
22,21,90,65,7.5,98.0,76,low risk
|
| 77 |
+
682,25,140,100,7.01,98.0,80,high risk
|
| 78 |
+
990,19,90,65,11.0,101.0,70,high risk
|
| 79 |
+
807,31,120,60,6.1,98.0,76,mid risk
|
| 80 |
+
955,40,140,100,18.0,98.0,90,high risk
|
| 81 |
+
518,19,90,70,7.5,98.0,80,low risk
|
| 82 |
+
475,19,120,80,7.0,98.0,70,mid risk
|
| 83 |
+
782,25,120,80,6.8,98.0,66,mid risk
|
| 84 |
+
235,28,120,80,9.0,102.0,76,high risk
|
| 85 |
+
249,25,120,90,15.0,98.0,80,high risk
|
| 86 |
+
441,23,130,70,6.8,98.0,78,mid risk
|
| 87 |
+
211,35,100,70,7.0,98.0,60,low risk
|
| 88 |
+
75,23,130,70,6.9,98.0,70,mid risk
|
| 89 |
+
195,30,120,80,6.1,98.0,70,low risk
|
| 90 |
+
580,24,120,80,7.5,98.0,66,low risk
|
| 91 |
+
499,16,120,75,7.9,98.0,7,low risk
|
| 92 |
+
806,25,120,80,7.9,98.0,66,mid risk
|
| 93 |
+
88,19,120,75,6.9,98.0,66,mid risk
|
| 94 |
+
809,29,130,70,7.9,98.0,78,mid risk
|
| 95 |
+
900,60,90,65,7.9,98.0,77,low risk
|
| 96 |
+
230,50,140,90,15.0,98.0,77,high risk
|
| 97 |
+
120,48,120,80,11.0,98.0,88,high risk
|
| 98 |
+
957,14,90,65,7.0,101.0,70,high risk
|
| 99 |
+
299,19,120,80,7.0,98.0,70,mid risk
|
| 100 |
+
131,32,140,90,18.0,98.0,88,high risk
|
| 101 |
+
614,50,120,80,15.0,98.0,70,high risk
|
| 102 |
+
675,35,140,90,13.0,98.0,70,high risk
|
| 103 |
+
903,32,120,90,7.9,98.0,70,low risk
|
| 104 |
+
643,39,110,70,7.9,98.0,80,mid risk
|
| 105 |
+
271,21,120,80,7.0,98.0,77,low risk
|
| 106 |
+
968,55,140,95,19.0,98.0,77,high risk
|
| 107 |
+
678,23,140,80,7.01,98.0,70,high risk
|
| 108 |
+
315,21,120,80,6.9,98.0,88,low risk
|
| 109 |
+
155,12,95,60,7.5,98.0,65,low risk
|
| 110 |
+
339,65,120,90,6.9,103.0,76,low risk
|
| 111 |
+
827,12,90,60,7.5,102.0,66,mid risk
|
| 112 |
+
584,32,140,90,18.0,98.0,88,high risk
|
| 113 |
+
233,20,110,60,7.0,100.0,70,mid risk
|
| 114 |
+
181,60,120,85,15.0,98.0,60,high risk
|
| 115 |
+
153,25,120,90,7.5,98.0,80,low risk
|
| 116 |
+
539,32,140,90,18.0,98.0,88,high risk
|
| 117 |
+
173,23,100,85,7.1,98.0,66,low risk
|
| 118 |
+
45,32,120,90,7.5,98.0,70,low risk
|
| 119 |
+
839,28,85,60,9.0,101.0,86,mid risk
|
| 120 |
+
694,31,120,60,6.1,98.0,76,low risk
|
| 121 |
+
413,50,130,80,16.0,102.0,76,mid risk
|
| 122 |
+
548,35,140,100,7.5,98.0,66,high risk
|
| 123 |
+
491,23,120,90,7.9,98.0,70,mid risk
|
| 124 |
+
1,35,140,90,13.0,98.0,70,high risk
|
| 125 |
+
854,19,120,80,7.0,98.0,70,mid risk
|
| 126 |
+
776,50,120,80,7.8,98.0,70,mid risk
|
| 127 |
+
612,17,90,65,7.5,103.0,67,mid risk
|
| 128 |
+
325,22,90,65,6.9,98.0,78,low risk
|
| 129 |
+
448,30,120,75,6.8,98.0,70,mid risk
|
| 130 |
+
529,23,120,75,8.0,98.0,70,mid risk
|
| 131 |
+
688,40,120,90,12.0,98.0,80,high risk
|
| 132 |
+
121,49,140,90,15.0,98.0,90,high risk
|
| 133 |
+
625,15,76,49,7.5,98.0,77,low risk
|
| 134 |
+
761,16,90,65,6.9,98.0,76,mid risk
|
| 135 |
+
52,35,100,70,7.0,98.0,60,low risk
|
| 136 |
+
203,19,120,80,7.0,98.0,70,mid risk
|
| 137 |
+
450,15,120,80,6.8,98.0,70,low risk
|
| 138 |
+
264,22,100,65,6.9,98.0,70,low risk
|
| 139 |
+
763,12,95,60,6.9,98.0,65,mid risk
|
| 140 |
+
562,23,120,90,7.5,98.0,60,low risk
|
| 141 |
+
831,21,120,80,7.5,98.0,77,mid risk
|
| 142 |
+
68,20,110,60,7.0,100.0,70,mid risk
|
| 143 |
+
738,13,90,65,7.9,101.0,80,mid risk
|
| 144 |
+
225,19,120,80,7.0,98.0,70,mid risk
|
| 145 |
+
964,35,140,100,8.0,98.0,66,high risk
|
| 146 |
+
387,31,120,60,6.1,98.0,76,low risk
|
| 147 |
+
837,19,120,85,9.0,98.0,60,mid risk
|
| 148 |
+
240,17,120,80,7.0,102.0,76,high risk
|
| 149 |
+
298,23,90,60,7.7,98.0,76,low risk
|
| 150 |
+
223,42,120,80,6.4,98.0,70,low risk
|
| 151 |
+
253,40,120,90,6.9,98.0,80,low risk
|
| 152 |
+
138,50,130,100,16.0,98.0,75,high risk
|
| 153 |
+
565,59,120,80,7.5,98.0,70,low risk
|
| 154 |
+
323,60,120,80,6.9,98.0,76,low risk
|
| 155 |
+
790,12,120,90,6.8,98.0,80,mid risk
|
| 156 |
+
929,16,100,70,7.5,98.0,80,low risk
|
| 157 |
+
843,17,90,60,9.0,102.0,86,mid risk
|
| 158 |
+
92,13,90,65,7.8,101.0,80,mid risk
|
| 159 |
+
615,34,110,70,7.0,98.0,80,high risk
|
| 160 |
+
300,15,75,49,7.7,98.0,77,low risk
|
| 161 |
+
875,32,120,65,6.0,101.0,76,mid risk
|
| 162 |
+
882,29,100,70,6.8,98.0,80,low risk
|
| 163 |
+
939,21,75,50,7.5,98.0,60,low risk
|
| 164 |
+
888,48,120,80,11.0,98.0,88,low risk
|
| 165 |
+
724,31,120,60,6.1,98.0,76,mid risk
|
| 166 |
+
775,28,115,60,7.8,101.0,86,mid risk
|
| 167 |
+
104,23,140,90,6.8,98.0,70,high risk
|
| 168 |
+
191,17,90,65,6.1,103.0,67,high risk
|
| 169 |
+
172,20,100,90,7.1,98.0,88,low risk
|
| 170 |
+
179,21,75,50,6.1,98.0,70,low risk
|
| 171 |
+
558,45,120,95,7.5,98.0,66,low risk
|
| 172 |
+
345,37,120,90,11.0,98.0,88,high risk
|
| 173 |
+
217,30,140,100,15.0,98.0,70,high risk
|
| 174 |
+
871,29,130,70,6.7,98.0,78,mid risk
|
| 175 |
+
307,35,100,70,6.9,98.0,60,low risk
|
| 176 |
+
914,17,120,80,7.5,102.0,76,low risk
|
| 177 |
+
722,50,140,80,6.7,98.0,70,mid risk
|
| 178 |
+
979,48,120,80,11.0,98.0,88,high risk
|
| 179 |
+
295,17,85,60,6.3,102.0,86,high risk
|
| 180 |
+
6,23,130,70,7.01,98.0,78,mid risk
|
| 181 |
+
639,19,120,60,7.0,98.4,70,low risk
|
| 182 |
+
867,28,85,60,9.0,101.0,86,mid risk
|
| 183 |
+
704,12,100,50,6.0,98.0,70,mid risk
|
| 184 |
+
285,13,90,65,9.0,101.0,80,high risk
|
| 185 |
+
917,60,90,65,7.5,98.0,77,low risk
|
| 186 |
+
284,22,120,85,7.7,98.0,88,low risk
|
| 187 |
+
850,12,100,50,6.4,98.0,70,mid risk
|
| 188 |
+
590,23,100,85,7.5,98.0,66,mid risk
|
| 189 |
+
726,17,85,60,9.0,102.0,86,mid risk
|
| 190 |
+
664,21,90,50,6.9,98.0,60,low risk
|
| 191 |
+
787,50,130,80,16.0,102.0,76,mid risk
|
| 192 |
+
85,18,90,60,6.9,98.0,70,mid risk
|
| 193 |
+
163,21,120,80,7.5,98.0,76,low risk
|
| 194 |
+
34,21,75,50,6.1,98.0,70,low risk
|
| 195 |
+
124,32,140,90,18.0,98.0,88,high risk
|
| 196 |
+
467,50,140,90,15.0,98.0,90,high risk
|
| 197 |
+
752,29,130,70,7.7,98.0,78,mid risk
|
| 198 |
+
176,35,140,100,8.0,98.0,66,high risk
|
| 199 |
+
519,30,140,100,15.0,98.0,70,high risk
|
| 200 |
+
426,40,140,100,13.0,101.0,66,high risk
|
| 201 |
+
399,25,120,80,7.8,98.0,66,low risk
|
| 202 |
+
504,30,120,80,7.9,101.0,76,high risk
|
| 203 |
+
981,25,140,100,7.2,98.0,80,high risk
|
| 204 |
+
370,21,75,50,7.8,98.0,60,low risk
|
README.md
ADDED
|
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|
| 1 |
+
|
| 2 |
+
# Model description
|
| 3 |
+
|
| 4 |
+
This is a Decision tree model.
|
| 5 |
+
|
| 6 |
+
## Intended uses & limitations
|
| 7 |
+
|
| 8 |
+
This model is made for educational purposes and is not suitable for real world deployment due to biased predictions.
|
| 9 |
+
|
| 10 |
+
## Training Procedure
|
| 11 |
+
|
| 12 |
+
[More Information Needed]
|
| 13 |
+
|
| 14 |
+
### Hyperparameters
|
| 15 |
+
|
| 16 |
+
<details>
|
| 17 |
+
<summary> Click to expand </summary>
|
| 18 |
+
|
| 19 |
+
| Hyperparameter | Value |
|
| 20 |
+
| :----------------------: | :---: |
|
| 21 |
+
| ccp_alpha | 0.0 |
|
| 22 |
+
| class_weight | None |
|
| 23 |
+
| criterion | gini |
|
| 24 |
+
| max_depth | 3 |
|
| 25 |
+
| max_features | None |
|
| 26 |
+
| max_leaf_nodes | None |
|
| 27 |
+
| min_impurity_decrease | 0.0 |
|
| 28 |
+
| min_samples_leaf | 2 |
|
| 29 |
+
| min_samples_split | 2 |
|
| 30 |
+
| min_weight_fraction_leaf | 0.0 |
|
| 31 |
+
| monotonic_cst | None |
|
| 32 |
+
| random_state | 100 |
|
| 33 |
+
| splitter | best |
|
| 34 |
+
|
| 35 |
+
</details>
|
| 36 |
+
|
| 37 |
+
### Model Plot
|
| 38 |
+
|
| 39 |
+
<style>#sk-container-id-3 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: #000;--sklearn-color-text-muted: #666;--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;}
|
| 40 |
+
}#sk-container-id-3 {color: var(--sklearn-color-text);
|
| 41 |
+
}#sk-container-id-3 pre {padding: 0;
|
| 42 |
+
}#sk-container-id-3 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;
|
| 43 |
+
}#sk-container-id-3 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);
|
| 44 |
+
}#sk-container-id-3 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;
|
| 45 |
+
}#sk-container-id-3 div.sk-text-repr-fallback {display: none;
|
| 46 |
+
}div.sk-parallel-item,
|
| 47 |
+
div.sk-serial,
|
| 48 |
+
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;
|
| 49 |
+
}/* Parallel-specific style estimator block */#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
|
| 50 |
+
}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
|
| 51 |
+
}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;
|
| 52 |
+
}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
|
| 53 |
+
}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
|
| 54 |
+
}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;
|
| 55 |
+
}/* Serial-specific style estimator block */#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
|
| 56 |
+
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 57 |
+
clickable and can be expanded/collapsed.
|
| 58 |
+
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 59 |
+
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 60 |
+
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-3 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);
|
| 61 |
+
}/* Toggleable label */
|
| 62 |
+
#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: flex;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;align-items: start;justify-content: space-between;gap: 0.5em;
|
| 63 |
+
}#sk-container-id-3 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted);
|
| 64 |
+
}#sk-container-id-3 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);
|
| 65 |
+
}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
|
| 66 |
+
}/* Toggleable content - dropdown */#sk-container-id-3 div.sk-toggleable__content {display: none;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
|
| 67 |
+
}#sk-container-id-3 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 68 |
+
}#sk-container-id-3 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);
|
| 69 |
+
}#sk-container-id-3 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 70 |
+
}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */display: block;width: 100%;overflow: visible;
|
| 71 |
+
}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
|
| 72 |
+
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-3 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);
|
| 73 |
+
}#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
|
| 74 |
+
}/* Estimator-specific style *//* Colorize estimator box */
|
| 75 |
+
#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 76 |
+
}#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 77 |
+
}#sk-container-id-3 div.sk-label label.sk-toggleable__label,
|
| 78 |
+
#sk-container-id-3 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
|
| 79 |
+
}/* On hover, darken the color of the background */
|
| 80 |
+
#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
|
| 81 |
+
}/* Label box, darken color on hover, fitted */
|
| 82 |
+
#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
|
| 83 |
+
}/* Estimator label */#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
|
| 84 |
+
}#sk-container-id-3 div.sk-label-container {text-align: center;
|
| 85 |
+
}/* Estimator-specific */
|
| 86 |
+
#sk-container-id-3 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);
|
| 87 |
+
}#sk-container-id-3 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 88 |
+
}/* on hover */
|
| 89 |
+
#sk-container-id-3 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 90 |
+
}#sk-container-id-3 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 91 |
+
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
|
| 92 |
+
a:link.sk-estimator-doc-link,
|
| 93 |
+
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: 0.5em;text-align: center;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
|
| 94 |
+
}.sk-estimator-doc-link.fitted,
|
| 95 |
+
a:link.sk-estimator-doc-link.fitted,
|
| 96 |
+
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 97 |
+
}/* On hover */
|
| 98 |
+
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 99 |
+
.sk-estimator-doc-link:hover,
|
| 100 |
+
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 101 |
+
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 102 |
+
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 103 |
+
.sk-estimator-doc-link.fitted:hover,
|
| 104 |
+
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 105 |
+
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 106 |
+
}/* Span, style for the box shown on hovering the info icon */
|
| 107 |
+
.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);
|
| 108 |
+
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
|
| 109 |
+
}.sk-estimator-doc-link:hover span {display: block;
|
| 110 |
+
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-3 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;
|
| 111 |
+
}#sk-container-id-3 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 112 |
+
}/* On hover */
|
| 113 |
+
#sk-container-id-3 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 114 |
+
}#sk-container-id-3 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
|
| 115 |
+
}.estimator-table summary {padding: .5rem;font-family: monospace;cursor: pointer;
|
| 116 |
+
}.estimator-table details[open] {padding-left: 0.1rem;padding-right: 0.1rem;padding-bottom: 0.3rem;
|
| 117 |
+
}.estimator-table .parameters-table {margin-left: auto !important;margin-right: auto !important;
|
| 118 |
+
}.estimator-table .parameters-table tr:nth-child(odd) {background-color: #fff;
|
| 119 |
+
}.estimator-table .parameters-table tr:nth-child(even) {background-color: #f6f6f6;
|
| 120 |
+
}.estimator-table .parameters-table tr:hover {background-color: #e0e0e0;
|
| 121 |
+
}.estimator-table table td {border: 1px solid rgba(106, 105, 104, 0.232);
|
| 122 |
+
}.user-set td {color:rgb(255, 94, 0);text-align: left;
|
| 123 |
+
}.user-set td.value pre {color:rgb(255, 94, 0) !important;background-color: transparent !important;
|
| 124 |
+
}.default td {color: black;text-align: left;
|
| 125 |
+
}.user-set td i,
|
| 126 |
+
.default td i {color: black;
|
| 127 |
+
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}
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+
</style><body><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier(max_depth=3, min_samples_leaf=2, random_state=100)</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"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>DecisionTreeClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.tree.DecisionTreeClassifier.html">?<span>Documentation for DecisionTreeClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix=""><div class="estimator-table"><details><summary>Parameters</summary><table class="parameters-table"><tbody><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('criterion',this.parentElement.nextElementSibling)"></i></td><td class="param">criterion </td><td class="value">'gini'</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('splitter',this.parentElement.nextElementSibling)"></i></td><td class="param">splitter </td><td class="value">'best'</td></tr><tr class="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('max_depth',this.parentElement.nextElementSibling)"></i></td><td class="param">max_depth </td><td class="value">3</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('min_samples_split',this.parentElement.nextElementSibling)"></i></td><td class="param">min_samples_split </td><td class="value">2</td></tr><tr class="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('min_samples_leaf',this.parentElement.nextElementSibling)"></i></td><td class="param">min_samples_leaf </td><td class="value">2</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('min_weight_fraction_leaf',this.parentElement.nextElementSibling)"></i></td><td class="param">min_weight_fraction_leaf </td><td class="value">0.0</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('max_features',this.parentElement.nextElementSibling)"></i></td><td class="param">max_features </td><td class="value">None</td></tr><tr class="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('random_state',this.parentElement.nextElementSibling)"></i></td><td class="param">random_state </td><td class="value">100</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('max_leaf_nodes',this.parentElement.nextElementSibling)"></i></td><td class="param">max_leaf_nodes </td><td class="value">None</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('min_impurity_decrease',this.parentElement.nextElementSibling)"></i></td><td class="param">min_impurity_decrease </td><td class="value">0.0</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('class_weight',this.parentElement.nextElementSibling)"></i></td><td class="param">class_weight </td><td class="value">None</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('ccp_alpha',this.parentElement.nextElementSibling)"></i></td><td class="param">ccp_alpha </td><td class="value">0.0</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('monotonic_cst',this.parentElement.nextElementSibling)"></i></td><td class="param">monotonic_cst </td><td class="value">None</td></tr></tbody></table></details></div></div></div></div></div></div><script>function copyToClipboard(text, element) {// Get the parameter prefix from the closest toggleable contentconst toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;const originalStyle = element.style;const computedStyle = window.getComputedStyle(element);const originalWidth = computedStyle.width;const originalHTML = element.innerHTML.replace('Copied!', '');navigator.clipboard.writeText(fullParamName).then(() => {element.style.width = originalWidth;element.style.color = 'green';element.innerHTML = "Copied!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);}).catch(err => {console.error('Failed to copy:', err);element.style.color = 'red';element.innerHTML = "Failed!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);});return false;
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+
}document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {const toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const paramName = element.parentElement.nextElementSibling.textContent.trim();const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;element.setAttribute('title', fullParamName);
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+
});
|
| 132 |
+
</script></body>
|
| 133 |
+
|
| 134 |
+
## Evaluation Results
|
| 135 |
+
|
| 136 |
+
[More Information Needed]
|
| 137 |
+
|
| 138 |
+
# How to Get Started with the Model
|
| 139 |
+
|
| 140 |
+
[More Information Needed]
|
| 141 |
+
|
| 142 |
+
# Model Card Authors
|
| 143 |
+
|
| 144 |
+
Richard S. Montgomery III
|
| 145 |
+
|
| 146 |
+
# Model Card Contact
|
| 147 |
+
|
| 148 |
+
You can contact the model card authors through following channels:
|
| 149 |
+
[More Information Needed]
|
| 150 |
+
|
| 151 |
+
# Citation
|
| 152 |
+
|
| 153 |
+
Below you can find information related to citation.
|
| 154 |
+
|
| 155 |
+
**BibTeX:**
|
| 156 |
+
```
|
| 157 |
+
[More Information Needed]
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
# Intended uses & limitations
|
| 161 |
+
|
| 162 |
+
This model is made for educational purposes and is not suitable for real world deployment due to biased predictions.
|
| 163 |
+
|
| 164 |
+
# Features
|
| 165 |
+
|
| 166 |
+
SystolicBP
|
| 167 |
+
DiastolicBP
|
| 168 |
+
BS
|
| 169 |
+
BodyTemp
|
| 170 |
+
HeartRate
|
| 171 |
+
RiskLevel
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
# Hyperparameters
|
| 175 |
+
|
| 176 |
+
max_depth: 3
|
| 177 |
+
sin_samples_leaf: 2
|
| 178 |
+
random_state: 100
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
# Evaluation Results
|
| 182 |
+
|
| 183 |
+
Accuracy: 0.65
|
| 184 |
+
precision_avg: 0.68
|
| 185 |
+
recall_avg: 0.67
|
| 186 |
+
|
config.json
ADDED
|
@@ -0,0 +1 @@
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|
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| 1 |
+
{"sklearn": {"columns": ["Age", "SystolicBP", "DiastolicBP", "BS", "BodyTemp", "HeartRate", "RiskLevel"], "environment": ["scikit-learn=1.0.2"], "example_input": {"Age": [25, 35, 29], "SystolicBP": [130, 140, 90], "DiastolicBP": [80, 90, 70], "BS": [15.0, 13.0, 8.0], "BodyTemp": [98.0, 98.0, 100.0], "HeartRate": [86, 70, 80], "RiskLevel": ["low_risk", "mid_risk", "high_risk"]}, "model": {"file": "model.pkl"}, "task": "tabular-classification"}}
|
model.pkl
ADDED
|
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|
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+
version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:d3a8dff13430079b89817b7d3799373a0303e8fcc518860c22fccc8fe59b2fd8
|
| 3 |
+
size 17101
|