Commit
•
7ecf383
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Parent(s):
4367535
Initial commit
Browse files- README.md +314 -0
- config.json +200 -0
- ubcv_grade_predictor_ridge.joblib +3 -0
README.md
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| 1 |
+
---
|
| 2 |
+
library_name: sklearn
|
| 3 |
+
tags:
|
| 4 |
+
- sklearn
|
| 5 |
+
- skops
|
| 6 |
+
- tabular-regression
|
| 7 |
+
model_format: pickle
|
| 8 |
+
model_file: ubcv_grade_predictor_ridge.joblib
|
| 9 |
+
widget:
|
| 10 |
+
- structuredData:
|
| 11 |
+
Campus:
|
| 12 |
+
- UBCV
|
| 13 |
+
Course:
|
| 14 |
+
- 110
|
| 15 |
+
CourseLevel:
|
| 16 |
+
- 1
|
| 17 |
+
Course_Avg_Roll_1y:
|
| 18 |
+
- 73.5864074445
|
| 19 |
+
Course_Max_Last_3y:
|
| 20 |
+
- 91.0
|
| 21 |
+
Course_Min_Last_3y:
|
| 22 |
+
- 71.898305085
|
| 23 |
+
Course_Std_Last_3y:
|
| 24 |
+
- 7.270712022509893
|
| 25 |
+
Prev_50-54:
|
| 26 |
+
- 1.0
|
| 27 |
+
Prev_55-59:
|
| 28 |
+
- 5.0
|
| 29 |
+
Prev_60-63:
|
| 30 |
+
- 11.0
|
| 31 |
+
Prev_64-67:
|
| 32 |
+
- 12.0
|
| 33 |
+
Prev_68-71:
|
| 34 |
+
- 14.0
|
| 35 |
+
Prev_72-75:
|
| 36 |
+
- 15.0
|
| 37 |
+
Prev_76-79:
|
| 38 |
+
- 11.0
|
| 39 |
+
Prev_80-84:
|
| 40 |
+
- 31.0
|
| 41 |
+
Prev_85-89:
|
| 42 |
+
- 23.0
|
| 43 |
+
Prev_90-100:
|
| 44 |
+
- 23.0
|
| 45 |
+
Prev_<50:
|
| 46 |
+
- 31.0
|
| 47 |
+
Prev_High:
|
| 48 |
+
- 97.0
|
| 49 |
+
Prev_Low:
|
| 50 |
+
- 5.0
|
| 51 |
+
Prev_Median:
|
| 52 |
+
- .nan
|
| 53 |
+
Prev_Percentile (25):
|
| 54 |
+
- .nan
|
| 55 |
+
Prev_Percentile (75):
|
| 56 |
+
- .nan
|
| 57 |
+
Prof_Courses_Taught:
|
| 58 |
+
- .nan
|
| 59 |
+
Prof_Prev_50-54:
|
| 60 |
+
- .nan
|
| 61 |
+
Prof_Prev_55-59:
|
| 62 |
+
- .nan
|
| 63 |
+
Prof_Prev_60-63:
|
| 64 |
+
- .nan
|
| 65 |
+
Prof_Prev_64-67:
|
| 66 |
+
- .nan
|
| 67 |
+
Prof_Prev_68-71:
|
| 68 |
+
- .nan
|
| 69 |
+
Prof_Prev_72-75:
|
| 70 |
+
- .nan
|
| 71 |
+
Prof_Prev_76-79:
|
| 72 |
+
- .nan
|
| 73 |
+
Prof_Prev_80-84:
|
| 74 |
+
- .nan
|
| 75 |
+
Prof_Prev_85-89:
|
| 76 |
+
- .nan
|
| 77 |
+
Prof_Prev_90-100:
|
| 78 |
+
- .nan
|
| 79 |
+
Prof_Prev_<50:
|
| 80 |
+
- .nan
|
| 81 |
+
Prof_Prev_High:
|
| 82 |
+
- .nan
|
| 83 |
+
Prof_Prev_Low:
|
| 84 |
+
- .nan
|
| 85 |
+
Prof_Prev_Median:
|
| 86 |
+
- .nan
|
| 87 |
+
Prof_Prev_Percentile (25):
|
| 88 |
+
- .nan
|
| 89 |
+
Prof_Prev_Percentile (75):
|
| 90 |
+
- .nan
|
| 91 |
+
Professor:
|
| 92 |
+
- ''
|
| 93 |
+
Session:
|
| 94 |
+
- W
|
| 95 |
+
Subject:
|
| 96 |
+
- CPSC
|
| 97 |
+
SubjectCourse:
|
| 98 |
+
- CPSC110
|
| 99 |
+
Year:
|
| 100 |
+
- 2018
|
| 101 |
+
Years_Since_Start:
|
| 102 |
+
- 4
|
| 103 |
+
---
|
| 104 |
+
|
| 105 |
+
# Model description
|
| 106 |
+
|
| 107 |
+
[More Information Needed]
|
| 108 |
+
|
| 109 |
+
## Intended uses & limitations
|
| 110 |
+
|
| 111 |
+
[More Information Needed]
|
| 112 |
+
|
| 113 |
+
## Training Procedure
|
| 114 |
+
|
| 115 |
+
[More Information Needed]
|
| 116 |
+
|
| 117 |
+
### Hyperparameters
|
| 118 |
+
|
| 119 |
+
<details>
|
| 120 |
+
<summary> Click to expand </summary>
|
| 121 |
+
|
| 122 |
+
| Hyperparameter | Value |
|
| 123 |
+
|----------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 124 |
+
| memory | |
|
| 125 |
+
| steps | [('columntransformer', ColumnTransformer(transformers=[('pipeline-1',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer()),<br /> ('standardscaler',<br /> StandardScaler())]),<br /> ['Course_Avg_Roll_1y', 'Course_Min_Last_3y',<br /> 'Course_Max_Last_3y', 'Course_Std_Last_3y']),<br /> ('pipeline-2',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_b...<br /> SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]),<br /> ['CourseLevel', 'Years_Since_Start',<br /> 'Prof_Courses_Taught', 'Year']),<br /> ('drop', 'drop',<br /> ['Reported', 'Section', 'Detail', 'Median',<br /> 'Percentile (25)', 'Percentile (75)', 'High',<br /> 'Low', '<50', '50-54', '55-59', '60-63',<br /> '64-67', '68-71', '72-75', '76-79', '80-84',<br /> '85-89', '90-100'])])), ('ridge', Ridge(alpha=2.091, random_state=42))] |
|
| 126 |
+
| transform_input | |
|
| 127 |
+
| verbose | False |
|
| 128 |
+
| columntransformer | ColumnTransformer(transformers=[('pipeline-1',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer()),<br /> ('standardscaler',<br /> StandardScaler())]),<br /> ['Course_Avg_Roll_1y', 'Course_Min_Last_3y',<br /> 'Course_Max_Last_3y', 'Course_Std_Last_3y']),<br /> ('pipeline-2',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_b...<br /> SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]),<br /> ['CourseLevel', 'Years_Since_Start',<br /> 'Prof_Courses_Taught', 'Year']),<br /> ('drop', 'drop',<br /> ['Reported', 'Section', 'Detail', 'Median',<br /> 'Percentile (25)', 'Percentile (75)', 'High',<br /> 'Low', '<50', '50-54', '55-59', '60-63',<br /> '64-67', '68-71', '72-75', '76-79', '80-84',<br /> '85-89', '90-100'])]) |
|
| 129 |
+
| ridge | Ridge(alpha=2.091, random_state=42) |
|
| 130 |
+
| columntransformer__force_int_remainder_cols | True |
|
| 131 |
+
| columntransformer__n_jobs | |
|
| 132 |
+
| columntransformer__remainder | drop |
|
| 133 |
+
| columntransformer__sparse_threshold | 0.3 |
|
| 134 |
+
| columntransformer__transformer_weights | |
|
| 135 |
+
| columntransformer__transformers | [('pipeline-1', Pipeline(steps=[('simpleimputer', SimpleImputer()),<br /> ('standardscaler', StandardScaler())]), ['Course_Avg_Roll_1y', 'Course_Min_Last_3y', 'Course_Max_Last_3y', 'Course_Std_Last_3y']), ('pipeline-2', Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_binary', handle_unknown='ignore'))]), ['Campus', 'Session', 'SubjectCourse', 'Professor', 'Subject']), ('pipeline-3', Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]), ['CourseLevel', 'Years_Since_Start', 'Prof_Courses_Taught', 'Year']), ('drop', 'drop', ['Reported', 'Section', 'Detail', 'Median', 'Percentile (25)', 'Percentile (75)', 'High', 'Low', '<50', '50-54', '55-59', '60-63', '64-67', '68-71', '72-75', '76-79', '80-84', '85-89', '90-100'])] |
|
| 136 |
+
| columntransformer__verbose | False |
|
| 137 |
+
| columntransformer__verbose_feature_names_out | True |
|
| 138 |
+
| columntransformer__pipeline-1 | Pipeline(steps=[('simpleimputer', SimpleImputer()),<br /> ('standardscaler', StandardScaler())]) |
|
| 139 |
+
| columntransformer__pipeline-2 | Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_binary', handle_unknown='ignore'))]) |
|
| 140 |
+
| columntransformer__pipeline-3 | Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]) |
|
| 141 |
+
| columntransformer__drop | drop |
|
| 142 |
+
| columntransformer__pipeline-1__memory | |
|
| 143 |
+
| columntransformer__pipeline-1__steps | [('simpleimputer', SimpleImputer()), ('standardscaler', StandardScaler())] |
|
| 144 |
+
| columntransformer__pipeline-1__transform_input | |
|
| 145 |
+
| columntransformer__pipeline-1__verbose | False |
|
| 146 |
+
| columntransformer__pipeline-1__simpleimputer | SimpleImputer() |
|
| 147 |
+
| columntransformer__pipeline-1__standardscaler | StandardScaler() |
|
| 148 |
+
| columntransformer__pipeline-1__simpleimputer__add_indicator | False |
|
| 149 |
+
| columntransformer__pipeline-1__simpleimputer__copy | True |
|
| 150 |
+
| columntransformer__pipeline-1__simpleimputer__fill_value | |
|
| 151 |
+
| columntransformer__pipeline-1__simpleimputer__keep_empty_features | False |
|
| 152 |
+
| columntransformer__pipeline-1__simpleimputer__missing_values | nan |
|
| 153 |
+
| columntransformer__pipeline-1__simpleimputer__strategy | mean |
|
| 154 |
+
| columntransformer__pipeline-1__standardscaler__copy | True |
|
| 155 |
+
| columntransformer__pipeline-1__standardscaler__with_mean | True |
|
| 156 |
+
| columntransformer__pipeline-1__standardscaler__with_std | True |
|
| 157 |
+
| columntransformer__pipeline-2__memory | |
|
| 158 |
+
| columntransformer__pipeline-2__steps | [('simpleimputer', SimpleImputer(strategy='most_frequent')), ('onehotencoder', OneHotEncoder(drop='if_binary', handle_unknown='ignore'))] |
|
| 159 |
+
| columntransformer__pipeline-2__transform_input | |
|
| 160 |
+
| columntransformer__pipeline-2__verbose | False |
|
| 161 |
+
| columntransformer__pipeline-2__simpleimputer | SimpleImputer(strategy='most_frequent') |
|
| 162 |
+
| columntransformer__pipeline-2__onehotencoder | OneHotEncoder(drop='if_binary', handle_unknown='ignore') |
|
| 163 |
+
| columntransformer__pipeline-2__simpleimputer__add_indicator | False |
|
| 164 |
+
| columntransformer__pipeline-2__simpleimputer__copy | True |
|
| 165 |
+
| columntransformer__pipeline-2__simpleimputer__fill_value | |
|
| 166 |
+
| columntransformer__pipeline-2__simpleimputer__keep_empty_features | False |
|
| 167 |
+
| columntransformer__pipeline-2__simpleimputer__missing_values | nan |
|
| 168 |
+
| columntransformer__pipeline-2__simpleimputer__strategy | most_frequent |
|
| 169 |
+
| columntransformer__pipeline-2__onehotencoder__categories | auto |
|
| 170 |
+
| columntransformer__pipeline-2__onehotencoder__drop | if_binary |
|
| 171 |
+
| columntransformer__pipeline-2__onehotencoder__dtype | <class 'numpy.float64'> |
|
| 172 |
+
| columntransformer__pipeline-2__onehotencoder__feature_name_combiner | concat |
|
| 173 |
+
| columntransformer__pipeline-2__onehotencoder__handle_unknown | ignore |
|
| 174 |
+
| columntransformer__pipeline-2__onehotencoder__max_categories | |
|
| 175 |
+
| columntransformer__pipeline-2__onehotencoder__min_frequency | |
|
| 176 |
+
| columntransformer__pipeline-2__onehotencoder__sparse_output | True |
|
| 177 |
+
| columntransformer__pipeline-3__memory | |
|
| 178 |
+
| columntransformer__pipeline-3__steps | [('simpleimputer', SimpleImputer(strategy='most_frequent')), ('ordinalencoder', OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1))] |
|
| 179 |
+
| columntransformer__pipeline-3__transform_input | |
|
| 180 |
+
| columntransformer__pipeline-3__verbose | False |
|
| 181 |
+
| columntransformer__pipeline-3__simpleimputer | SimpleImputer(strategy='most_frequent') |
|
| 182 |
+
| columntransformer__pipeline-3__ordinalencoder | OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1) |
|
| 183 |
+
| columntransformer__pipeline-3__simpleimputer__add_indicator | False |
|
| 184 |
+
| columntransformer__pipeline-3__simpleimputer__copy | True |
|
| 185 |
+
| columntransformer__pipeline-3__simpleimputer__fill_value | |
|
| 186 |
+
| columntransformer__pipeline-3__simpleimputer__keep_empty_features | False |
|
| 187 |
+
| columntransformer__pipeline-3__simpleimputer__missing_values | nan |
|
| 188 |
+
| columntransformer__pipeline-3__simpleimputer__strategy | most_frequent |
|
| 189 |
+
| columntransformer__pipeline-3__ordinalencoder__categories | auto |
|
| 190 |
+
| columntransformer__pipeline-3__ordinalencoder__dtype | <class 'numpy.float64'> |
|
| 191 |
+
| columntransformer__pipeline-3__ordinalencoder__encoded_missing_value | nan |
|
| 192 |
+
| columntransformer__pipeline-3__ordinalencoder__handle_unknown | use_encoded_value |
|
| 193 |
+
| columntransformer__pipeline-3__ordinalencoder__max_categories | |
|
| 194 |
+
| columntransformer__pipeline-3__ordinalencoder__min_frequency | |
|
| 195 |
+
| columntransformer__pipeline-3__ordinalencoder__unknown_value | -1 |
|
| 196 |
+
| ridge__alpha | 2.091 |
|
| 197 |
+
| ridge__copy_X | True |
|
| 198 |
+
| ridge__fit_intercept | True |
|
| 199 |
+
| ridge__max_iter | |
|
| 200 |
+
| ridge__positive | False |
|
| 201 |
+
| ridge__random_state | 42 |
|
| 202 |
+
| ridge__solver | auto |
|
| 203 |
+
| ridge__tol | 0.0001 |
|
| 204 |
+
|
| 205 |
+
</details>
|
| 206 |
+
|
| 207 |
+
### Model Plot
|
| 208 |
+
|
| 209 |
+
<style>#sk-container-id-1 {/* 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;}
|
| 210 |
+
}#sk-container-id-1 {color: var(--sklearn-color-text);
|
| 211 |
+
}#sk-container-id-1 pre {padding: 0;
|
| 212 |
+
}#sk-container-id-1 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;
|
| 213 |
+
}#sk-container-id-1 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);
|
| 214 |
+
}#sk-container-id-1 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;
|
| 215 |
+
}#sk-container-id-1 div.sk-text-repr-fallback {display: none;
|
| 216 |
+
}div.sk-parallel-item,
|
| 217 |
+
div.sk-serial,
|
| 218 |
+
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;
|
| 219 |
+
}/* Parallel-specific style estimator block */#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
|
| 220 |
+
}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
|
| 221 |
+
}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;
|
| 222 |
+
}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
|
| 223 |
+
}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
|
| 224 |
+
}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;
|
| 225 |
+
}/* Serial-specific style estimator block */#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
|
| 226 |
+
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
| 227 |
+
clickable and can be expanded/collapsed.
|
| 228 |
+
- Pipeline and ColumnTransformer use this feature and define the default style
|
| 229 |
+
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
| 230 |
+
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-1 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);
|
| 231 |
+
}/* Toggleable label */
|
| 232 |
+
#sk-container-id-1 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;
|
| 233 |
+
}#sk-container-id-1 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted);
|
| 234 |
+
}#sk-container-id-1 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);
|
| 235 |
+
}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
|
| 236 |
+
}/* Toggleable content - dropdown */#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
|
| 237 |
+
}#sk-container-id-1 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 238 |
+
}#sk-container-id-1 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);
|
| 239 |
+
}#sk-container-id-1 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 240 |
+
}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
|
| 241 |
+
}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
|
| 242 |
+
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-1 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);
|
| 243 |
+
}#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
|
| 244 |
+
}/* Estimator-specific style *//* Colorize estimator box */
|
| 245 |
+
#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 246 |
+
}#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 247 |
+
}#sk-container-id-1 div.sk-label label.sk-toggleable__label,
|
| 248 |
+
#sk-container-id-1 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
|
| 249 |
+
}/* On hover, darken the color of the background */
|
| 250 |
+
#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
|
| 251 |
+
}/* Label box, darken color on hover, fitted */
|
| 252 |
+
#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
|
| 253 |
+
}/* Estimator label */#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
|
| 254 |
+
}#sk-container-id-1 div.sk-label-container {text-align: center;
|
| 255 |
+
}/* Estimator-specific */
|
| 256 |
+
#sk-container-id-1 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);
|
| 257 |
+
}#sk-container-id-1 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
|
| 258 |
+
}/* on hover */
|
| 259 |
+
#sk-container-id-1 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
|
| 260 |
+
}#sk-container-id-1 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
|
| 261 |
+
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
|
| 262 |
+
a:link.sk-estimator-doc-link,
|
| 263 |
+
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);
|
| 264 |
+
}.sk-estimator-doc-link.fitted,
|
| 265 |
+
a:link.sk-estimator-doc-link.fitted,
|
| 266 |
+
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 267 |
+
}/* On hover */
|
| 268 |
+
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
| 269 |
+
.sk-estimator-doc-link:hover,
|
| 270 |
+
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
| 271 |
+
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 272 |
+
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
| 273 |
+
.sk-estimator-doc-link.fitted:hover,
|
| 274 |
+
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
| 275 |
+
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 276 |
+
}/* Span, style for the box shown on hovering the info icon */
|
| 277 |
+
.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);
|
| 278 |
+
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
|
| 279 |
+
}.sk-estimator-doc-link:hover span {display: block;
|
| 280 |
+
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-1 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;
|
| 281 |
+
}#sk-container-id-1 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
|
| 282 |
+
}/* On hover */
|
| 283 |
+
#sk-container-id-1 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
|
| 284 |
+
}#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
|
| 285 |
+
}
|
| 286 |
+
</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('pipeline-1',Pipeline(steps=[('simpleimputer',SimpleImputer()),('standardscaler',StandardScaler())]),['Course_Avg_Roll_1y','Course_Min_Last_3y','Course_Max_Last_3y','Course_Std_Last_3y']),('pipeline-2',Pipeline(steps=[('simpleimputer',SimpleImputer(strategy='most_frequent')),('on...OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1))]),['CourseLevel','Years_Since_Start','Prof_Courses_Taught','Year']),('drop', 'drop',['Reported', 'Section','Detail', 'Median','Percentile (25)','Percentile (75)', 'High','Low', '<50', '50-54','55-59', '60-63', '64-67','68-71', '72-75', '76-79','80-84', '85-89','90-100'])])),('ridge', Ridge(alpha=2.091, random_state=42))])</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-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Pipeline</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('pipeline-1',Pipeline(steps=[('simpleimputer',SimpleImputer()),('standardscaler',StandardScaler())]),['Course_Avg_Roll_1y','Course_Min_Last_3y','Course_Max_Last_3y','Course_Std_Last_3y']),('pipeline-2',Pipeline(steps=[('simpleimputer',SimpleImputer(strategy='most_frequent')),('on...OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1))]),['CourseLevel','Years_Since_Start','Prof_Courses_Taught','Year']),('drop', 'drop',['Reported', 'Section','Detail', 'Median','Percentile (25)','Percentile (75)', 'High','Low', '<50', '50-54','55-59', '60-63', '64-67','68-71', '72-75', '76-79','80-84', '85-89','90-100'])])),('ridge', Ridge(alpha=2.091, random_state=42))])</pre></div> </div></div><div class="sk-serial"><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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>columntransformer: ColumnTransformer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>ColumnTransformer(transformers=[('pipeline-1',Pipeline(steps=[('simpleimputer',SimpleImputer()),('standardscaler',StandardScaler())]),['Course_Avg_Roll_1y', 'Course_Min_Last_3y','Course_Max_Last_3y', 'Course_Std_Last_3y']),('pipeline-2',Pipeline(steps=[('simpleimputer',SimpleImputer(strategy='most_frequent')),('onehotencoder',OneHotEncoder(drop='if_b...SimpleImputer(strategy='most_frequent')),('ordinalencoder',OrdinalEncoder(handle_unknown='use_encoded_value',unknown_value=-1))]),['CourseLevel', 'Years_Since_Start','Prof_Courses_Taught', 'Year']),('drop', 'drop',['Reported', 'Section', 'Detail', 'Median','Percentile (25)', 'Percentile (75)', 'High','Low', '<50', '50-54', '55-59', '60-63','64-67', '68-71', '72-75', '76-79', '80-84','85-89', '90-100'])])</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>pipeline-1</div></div></label><div class="sk-toggleable__content fitted"><pre>['Course_Avg_Roll_1y', 'Course_Min_Last_3y', 'Course_Max_Last_3y', 'Course_Std_Last_3y']</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><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-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer()</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-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>StandardScaler</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></div></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>pipeline-2</div></div></label><div class="sk-toggleable__content fitted"><pre>['Campus', 'Session', 'SubjectCourse', 'Professor', 'Subject']</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy='most_frequent')</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-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OneHotEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html">?<span>Documentation for OneHotEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OneHotEncoder(drop='if_binary', handle_unknown='ignore')</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>pipeline-3</div></div></label><div class="sk-toggleable__content fitted"><pre>['CourseLevel', 'Years_Since_Start', 'Prof_Courses_Taught', 'Year']</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><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-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>SimpleImputer</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></div></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy='most_frequent')</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-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>OrdinalEncoder</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OrdinalEncoder.html">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class="sk-toggleable__content fitted"><pre>OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)</pre></div> </div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>drop</div></div></label><div class="sk-toggleable__content fitted"><pre>['Reported', 'Section', 'Detail', 'Median', 'Percentile (25)', 'Percentile (75)', 'High', 'Low', '<50', '50-54', '55-59', '60-63', '64-67', '68-71', '72-75', '76-79', '80-84', '85-89', '90-100']</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-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>drop</div></div></label><div class="sk-toggleable__content fitted"><pre>drop</pre></div> </div></div></div></div></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-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Ridge</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.Ridge.html">?<span>Documentation for Ridge</span></a></div></label><div class="sk-toggleable__content fitted"><pre>Ridge(alpha=2.091, random_state=42)</pre></div> </div></div></div></div></div></div>
|
| 287 |
+
|
| 288 |
+
## Evaluation Results
|
| 289 |
+
|
| 290 |
+
[More Information Needed]
|
| 291 |
+
|
| 292 |
+
# How to Get Started with the Model
|
| 293 |
+
|
| 294 |
+
[More Information Needed]
|
| 295 |
+
|
| 296 |
+
# Model Card Authors
|
| 297 |
+
|
| 298 |
+
This model card is written by following authors:
|
| 299 |
+
|
| 300 |
+
[More Information Needed]
|
| 301 |
+
|
| 302 |
+
# Model Card Contact
|
| 303 |
+
|
| 304 |
+
You can contact the model card authors through following channels:
|
| 305 |
+
[More Information Needed]
|
| 306 |
+
|
| 307 |
+
# Citation
|
| 308 |
+
|
| 309 |
+
Below you can find information related to citation.
|
| 310 |
+
|
| 311 |
+
**BibTeX:**
|
| 312 |
+
```
|
| 313 |
+
[More Information Needed]
|
| 314 |
+
```
|
config.json
ADDED
|
@@ -0,0 +1,200 @@
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|
| 1 |
+
{
|
| 2 |
+
"sklearn": {
|
| 3 |
+
"columns": [
|
| 4 |
+
"Campus",
|
| 5 |
+
"Subject",
|
| 6 |
+
"Course",
|
| 7 |
+
"Year",
|
| 8 |
+
"Session",
|
| 9 |
+
"SubjectCourse",
|
| 10 |
+
"CourseLevel",
|
| 11 |
+
"Years_Since_Start",
|
| 12 |
+
"Course_Avg_Roll_1y",
|
| 13 |
+
"Course_Min_Last_3y",
|
| 14 |
+
"Course_Max_Last_3y",
|
| 15 |
+
"Course_Std_Last_3y",
|
| 16 |
+
"Professor",
|
| 17 |
+
"Prof_Courses_Taught",
|
| 18 |
+
"Prev_Median",
|
| 19 |
+
"Prof_Prev_Median",
|
| 20 |
+
"Prev_Percentile (25)",
|
| 21 |
+
"Prof_Prev_Percentile (25)",
|
| 22 |
+
"Prev_Percentile (75)",
|
| 23 |
+
"Prof_Prev_Percentile (75)",
|
| 24 |
+
"Prev_High",
|
| 25 |
+
"Prof_Prev_High",
|
| 26 |
+
"Prev_Low",
|
| 27 |
+
"Prof_Prev_Low",
|
| 28 |
+
"Prev_<50",
|
| 29 |
+
"Prof_Prev_<50",
|
| 30 |
+
"Prev_50-54",
|
| 31 |
+
"Prof_Prev_50-54",
|
| 32 |
+
"Prev_55-59",
|
| 33 |
+
"Prof_Prev_55-59",
|
| 34 |
+
"Prev_60-63",
|
| 35 |
+
"Prof_Prev_60-63",
|
| 36 |
+
"Prev_64-67",
|
| 37 |
+
"Prof_Prev_64-67",
|
| 38 |
+
"Prev_68-71",
|
| 39 |
+
"Prof_Prev_68-71",
|
| 40 |
+
"Prev_72-75",
|
| 41 |
+
"Prof_Prev_72-75",
|
| 42 |
+
"Prev_76-79",
|
| 43 |
+
"Prof_Prev_76-79",
|
| 44 |
+
"Prev_80-84",
|
| 45 |
+
"Prof_Prev_80-84",
|
| 46 |
+
"Prev_85-89",
|
| 47 |
+
"Prof_Prev_85-89",
|
| 48 |
+
"Prev_90-100",
|
| 49 |
+
"Prof_Prev_90-100"
|
| 50 |
+
],
|
| 51 |
+
"environment": [
|
| 52 |
+
"scikit-learn=1.6.0"
|
| 53 |
+
],
|
| 54 |
+
"example_input": {
|
| 55 |
+
"Campus": [
|
| 56 |
+
"UBCV"
|
| 57 |
+
],
|
| 58 |
+
"Course": [
|
| 59 |
+
110
|
| 60 |
+
],
|
| 61 |
+
"CourseLevel": [
|
| 62 |
+
1
|
| 63 |
+
],
|
| 64 |
+
"Course_Avg_Roll_1y": [
|
| 65 |
+
73.5864074445
|
| 66 |
+
],
|
| 67 |
+
"Course_Max_Last_3y": [
|
| 68 |
+
91.0
|
| 69 |
+
],
|
| 70 |
+
"Course_Min_Last_3y": [
|
| 71 |
+
71.898305085
|
| 72 |
+
],
|
| 73 |
+
"Course_Std_Last_3y": [
|
| 74 |
+
7.270712022509893
|
| 75 |
+
],
|
| 76 |
+
"Prev_50-54": [
|
| 77 |
+
1.0
|
| 78 |
+
],
|
| 79 |
+
"Prev_55-59": [
|
| 80 |
+
5.0
|
| 81 |
+
],
|
| 82 |
+
"Prev_60-63": [
|
| 83 |
+
11.0
|
| 84 |
+
],
|
| 85 |
+
"Prev_64-67": [
|
| 86 |
+
12.0
|
| 87 |
+
],
|
| 88 |
+
"Prev_68-71": [
|
| 89 |
+
14.0
|
| 90 |
+
],
|
| 91 |
+
"Prev_72-75": [
|
| 92 |
+
15.0
|
| 93 |
+
],
|
| 94 |
+
"Prev_76-79": [
|
| 95 |
+
11.0
|
| 96 |
+
],
|
| 97 |
+
"Prev_80-84": [
|
| 98 |
+
31.0
|
| 99 |
+
],
|
| 100 |
+
"Prev_85-89": [
|
| 101 |
+
23.0
|
| 102 |
+
],
|
| 103 |
+
"Prev_90-100": [
|
| 104 |
+
23.0
|
| 105 |
+
],
|
| 106 |
+
"Prev_<50": [
|
| 107 |
+
31.0
|
| 108 |
+
],
|
| 109 |
+
"Prev_High": [
|
| 110 |
+
97.0
|
| 111 |
+
],
|
| 112 |
+
"Prev_Low": [
|
| 113 |
+
5.0
|
| 114 |
+
],
|
| 115 |
+
"Prev_Median": [
|
| 116 |
+
NaN
|
| 117 |
+
],
|
| 118 |
+
"Prev_Percentile (25)": [
|
| 119 |
+
NaN
|
| 120 |
+
],
|
| 121 |
+
"Prev_Percentile (75)": [
|
| 122 |
+
NaN
|
| 123 |
+
],
|
| 124 |
+
"Prof_Courses_Taught": [
|
| 125 |
+
NaN
|
| 126 |
+
],
|
| 127 |
+
"Prof_Prev_50-54": [
|
| 128 |
+
NaN
|
| 129 |
+
],
|
| 130 |
+
"Prof_Prev_55-59": [
|
| 131 |
+
NaN
|
| 132 |
+
],
|
| 133 |
+
"Prof_Prev_60-63": [
|
| 134 |
+
NaN
|
| 135 |
+
],
|
| 136 |
+
"Prof_Prev_64-67": [
|
| 137 |
+
NaN
|
| 138 |
+
],
|
| 139 |
+
"Prof_Prev_68-71": [
|
| 140 |
+
NaN
|
| 141 |
+
],
|
| 142 |
+
"Prof_Prev_72-75": [
|
| 143 |
+
NaN
|
| 144 |
+
],
|
| 145 |
+
"Prof_Prev_76-79": [
|
| 146 |
+
NaN
|
| 147 |
+
],
|
| 148 |
+
"Prof_Prev_80-84": [
|
| 149 |
+
NaN
|
| 150 |
+
],
|
| 151 |
+
"Prof_Prev_85-89": [
|
| 152 |
+
NaN
|
| 153 |
+
],
|
| 154 |
+
"Prof_Prev_90-100": [
|
| 155 |
+
NaN
|
| 156 |
+
],
|
| 157 |
+
"Prof_Prev_<50": [
|
| 158 |
+
NaN
|
| 159 |
+
],
|
| 160 |
+
"Prof_Prev_High": [
|
| 161 |
+
NaN
|
| 162 |
+
],
|
| 163 |
+
"Prof_Prev_Low": [
|
| 164 |
+
NaN
|
| 165 |
+
],
|
| 166 |
+
"Prof_Prev_Median": [
|
| 167 |
+
NaN
|
| 168 |
+
],
|
| 169 |
+
"Prof_Prev_Percentile (25)": [
|
| 170 |
+
NaN
|
| 171 |
+
],
|
| 172 |
+
"Prof_Prev_Percentile (75)": [
|
| 173 |
+
NaN
|
| 174 |
+
],
|
| 175 |
+
"Professor": [
|
| 176 |
+
""
|
| 177 |
+
],
|
| 178 |
+
"Session": [
|
| 179 |
+
"W"
|
| 180 |
+
],
|
| 181 |
+
"Subject": [
|
| 182 |
+
"CPSC"
|
| 183 |
+
],
|
| 184 |
+
"SubjectCourse": [
|
| 185 |
+
"CPSC110"
|
| 186 |
+
],
|
| 187 |
+
"Year": [
|
| 188 |
+
2018
|
| 189 |
+
],
|
| 190 |
+
"Years_Since_Start": [
|
| 191 |
+
4
|
| 192 |
+
]
|
| 193 |
+
},
|
| 194 |
+
"model": {
|
| 195 |
+
"file": "ubcv_grade_predictor_ridge.joblib"
|
| 196 |
+
},
|
| 197 |
+
"model_format": "pickle",
|
| 198 |
+
"task": "tabular-regression"
|
| 199 |
+
}
|
| 200 |
+
}
|
ubcv_grade_predictor_ridge.joblib
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cd92bfd4cb60fc4a8922982e03c30497d3d94a724369e144f521e6d4f610306d
|
| 3 |
+
size 1954779
|